Keywords: Stochastic control, Machine learning
Abstract: Modern applications of control involve decision-making in complex interactive systems: intelligent transportation, smart power grid, and assistive robotics. To reliably apply automation to these tasks, we need to account for uncertainties arising from modelling complexities and the interactions in the subsystems. This gives rise to novel challenges for control, at the intersection of learning theory and game theory. This plenary will spotlight recent developments on advancing stochastic control, reinforcement learning, and multiagent control to address these challenges. I will highlight our technical contributions in learning safe controller s and multiagent equilibria. The talk will showcase examples of applying the developed theory to the real world and conclude by highlighting open research challenges.

Keywords: Control education, Computer aided learning, Control courses and labs
Abstract: In recent years the educationally focussed parts of the global control community have given some focus to what constitutes a sensible first course in control [19] and how to support this with high quality learning and teaching resources. This paper contributes to that overall effort in that it provides an example of high quality, open-access resources to support students in their independent learning. One aspect of the ideal f irst course in control is the suggestion that we (the community) focus more on concepts and understanding and less on tedious paper and pen calculations; to do this we need suitable easy to use software for performing computations and producing illustrations. Hence the author is leading what he hopes will be a collaborative community project on creating a MATLAB toolbox to provide such software. The purpose of this paper is to highlight the toolbox, present its current contents and thus enable staff to evaluate and adopt this toolbox and moreover, to reflect on how it might be improved.

Keywords: Control education, Control courses and labs, Robotics
Abstract: Control engineering and robotics hold significant potential to support the development of valuable skills that allow the comprehension and analysis of the current real-world problems. Unfortunately, usually, education on control engineering does not start before undergraduate courses. This paper reviews some of the current experiences whose aim is to introduce control engineering education in K12 education. Subsequently, it presents a whole curriculum based on Educational Robotics that could be integrated into primary school curricula to face control engineering education. One of the key aspects in the creation of such curriculum is the co-creation of the educational curriculum with teachers and education experts. Notably, empowering teachers is essential to effectively convey the fundamental concepts of control theory, enhancing students’ problem-solving and critical thinking skills in the domain of control engineering.

Keywords: Control education
Abstract: This paper proposes an adapted version of the classic Pong game, tailored for educational purposes and illustrated with two ground mobile robots and one drone, therefore called DroPong. The goal of this paper is twofold: (i) motivating students to pursue within a Control Engineering specialization, by involving engineering students in the development of game-inspired diverting Robotic platforms for teaching and research; (ii) educating future generations by increasing the visibility of Science, more particularly Control Engineering and Robotics, via popularizing science activities for children during open-day events and science festivals. The developed open-access resources are available on a dedicated repository.

Keywords: Control courses and labs, Autonomous robots, Cooperative control
Abstract: In the domain of Connected and Automated Vehicles (CAVs), small-scale testbeds bridge expensive testing in the real world and computer simulations. Meanwhile, they offer educational opportunities for students to acquire hands-on experience in areas like control, vehicle dynamics, trajectory planning, and real-time software. At RWTH Aachen University, we, the Cyber-Physical Mobility Group, built a small-scale testbed, the open-source and remotely accessible Cyber-Physical Mobility Lab (CPM Lab). We use it for one undergraduate course, one graduate course, and an international competition. Our literature research indicates that no similar publicly available testbed offers continuous educational applications for all academic levels, including postgraduate students. This paper presents (i) an educational umbrella concept designed to create a course portfolio suitable for undergraduate, graduate, and postgraduate needs, (ii) updates to the course concepts with an emphasis on previous publications, and (iii) lessons learned to develop an education portfolio based on small-scale testbeds. We base our results on evaluations conducted over four years involving over 370 students participating in our courses. Our findings indicate that small-scale testbeds can help students become more invested in the topic and may motivate them beyond course requirements.

Keywords: Control education, Control courses and labs
Abstract: This paper presents a software stack named setcmas, for Simulation and Experiments in Teaching Control of Multi-Agent Systems. It aims at facilitating simulations and experiments for teachers and students involved in Control Engineering curricula. The stack is available online and can be used freely for Control Education purposes. It leverages the advantages of ROS, the robot operating system, without requiring any knowledge of it to be used. Control algorithms for multi-agent systems can be implemented in simple Python scripts, and then simulated and validated through experiments with ground mobile robots. The paper presents the content of the stack, the possible architecture used for experiments, and some usages already made for teaching, demonstration, and research work.

Keywords: Control education
Abstract: Kahoot! is an online learning platform used to play and share interactive learning activities, quizzes, and questions. This paper presents the experiences with using Kahoot! in control theoretical courses at the University of Stavanger, Norway. An example of how to use Kahoot! within a 2-hours review of the Laplace transform is detailed. The discussion is supported by quantitative and qualitative data collected through a questionnaire and by the lecturer’s own reflections after reviewing the feedback received during the final course evaluations. The content of this paper can be relevant for lecturers of automatic control and other STEM-related courses that want to make a more extensive use of Kahoot! as an active learning tool that stimulates and engages students.

Keywords: Predictive control for linear systems, Aerospace, Constrained control
Abstract: The control strategy of complex engineering systems, e.g., in automotive, aeronautical and space applications, are oftentimes well-established and there is limited space to integrate major novelties in the design of control laws. As a consequence, the problem of managing the system constraints is addressed with overconservative and sub-optimal solutions. With a particular focus on satellite missions, this paper proposes a Reference Governor-based approach to design the optimal safe trajectory so as to exploit the full capabilities of an established closed-loop Multi-Input Multi-Output (MIMO) system subject to state and input constraints. While not interfering with its stability properties, the Governor predicts the future evolution of the closed-loop system and modifies the reference to track in case constraints are at risk. The computationally attractive Scalar Reference Governor is compared to the Vector Reference Governor, which is optimal for MIMO systems. Finally, a sub-optimal fast Governor is proposed for MIMO systems with limited coupling. Numerical simulations are run on the CNES high-fidelity simulator developed for the Microcarb mission and illustrate the advantages of the proposed methodology.

Keywords: Predictive control for linear systems, Automotive, Behavioural systems
Abstract: Articulated vehicles are susceptible to instability issues due to their distinct dynamic properties. Most existing control strategies focus on constructing an integrated model, yet an accurate parametric model for a complex nonlinear system might be unavailable. To address this, a bilevel control structure is established, with the upper level generating corrective yaw moments and the lower level focusing on control allocation, then data-driven predictive control method is introduced, which relies only on input/output measurements to construct a non-parametric representation of the system, this method is implemented in a receding-horizon manner similar to MPC, incorporating constraints to achieve safe maneuvering. The effectiveness of the proposed controller is presented by simulation results, which further confirm its potential in vehicle dynamics control.

Keywords: Predictive control for linear systems, Computational methods
Abstract: Computing the maximal (robust) positive invariant (M(R)PI) set for linear dynamics and a polyhedral constraint set is well known in the literature but, the effects and limitations of the different methods employed are not sufficiently clear, especially for high dimensional systems. In this paper we propose a systematic analysis of the existing techniques as well as the application of new ideas to accelerate the computation of the MPI set. This includes new stop conditions for the set recurrence that spans it. We analyze and compare these variations over a dynamical system whose dimension can be arbitrarily increased to draw conclusions about their relative strengths and weaknesses.

Keywords: Predictive control for linear systems, Distributed cooperative control over networks, Optimal control
Abstract: The use of communication technologies in advanced driver-assistance systems enables improving safety and control performance at the same time. Lane merging scenarios are typical traffic hubs which benefit from cooperative behavior but require guarantees on collision avoidance. To achieve these goals, we use a distributed model predictive control design with computationally efficient convex formulation of the optimal control problem. Safety guarantees are established through recursive feasibility using invariant terminal sets. Further, the framework of cooperative cost functions increases the global performance, such as the total time to merge, and maintains the formal guarantees. The performance of the proposed methods is illustrated by a numerical example, where the cooperative controller improves the overall cost by nearly 20%.

Keywords: Predictive control for linear systems, Optimal control
Abstract: This paper presents a control strategy to perform time-optimal point-to-point motions using the feasibility governor (FG) strategy. This technique consists of two stages. The first stage is a feasibility governor in which an auxiliary reference is calculated inside a feasible set that drives the system toward the reference along the constraint boundaries. The second stage is a time-optimal model predictive control (MPC) formulation with an appropriate structure which is executed in a set where the reference can be reached within the prediction horizon. Feasible and reachable sets that contain system dynamics and constraints information are computed offline, allowing the methodology to run with short prediction horizons. This reduces the computational load in the online execution. An example of a car represented by a bicycle model performing a lateral movement at constant forward velocity is presented to illustrate the controller's performance.

Keywords: Predictive control for linear systems, Optimization algorithms
Abstract: Model Predictive Control (MPC) for tracking formulation presents numerous advantages compared to standard MPC, such as a larger domain of attraction and recursive feasibility even when abrupt changes in the reference are produced. As a drawback, it includes some extra decision variables in its related optimization problem, leading to a semi-banded structure that differs from the banded structure encountered in standard MPC. This semi-banded structure prevents the direct use of the efficient algorithms available for banded problems. To address this issue, we present an algorithm based on the alternating direction method of multipliers that explicitly takes advantage of the underlying semi-banded structure of the MPC for tracking.

Keywords: Optimization, Distributed control, Optimization algorithms
Abstract: The shift from centralized to decentralized systems is increasing the complexity of many problems in control and optimization. However, it also presents the opportunity to exploit parallelized computational schemes. This paper shows how the solution process of mixed-integer problems, which often arise in areas like production scheduling or logistics, can be supported by employing parallel computations. To this end, dual variables are introduced that enable the decomposition of these complex problems into subproblems that can then be solved in parallel. The presented dual decomposition-based approach provides a lower bound for the optimal solution of the original problem, which can support the overall solution process. The focus of this paper is on the parallelizability of the computation of this lower bound. The bounds from three different dual decompositionbased distributed optimization algorithms are compared to the lower bounds provided by several commercial solvers within their branch-&-cut framework.

Keywords: Optimization, Filtering, Nonlinear system theory
Abstract: Optimization algorithms have a rich and fundamental relationship with ordinary differential equations given by its continuous-time limit. When the cost function varies with time -- typically in response to a dynamically changing environment -- online optimization becomes a continuous-time trajectory tracking problem. To accommodate these time variations, one typically requires some inherent knowledge about their nature such as a time derivative.

In this paper, we propose a novel construction and analysis of a continuous-time derivative estimation scheme based on "dirty-derivatives", and show how it naturally interfaces with continuous-time optimization algorithms using the language of ISS (Input-to-State Stability). More generally, we show how a simple Lyapunov redesign technique leads to provable suboptimality guarantees when composing this estimator with any well-behaved optimization algorithm for time-varying costs.

Keywords: Optimization, Machine learning, Neural networks
Abstract: Bayesian optimisation is an emerging machine learning technique known to be efficient especially for optimising functions which are expensive to evaluate. In Bayesian optimisation, a Gaussian process model of the unknown function is identified based on available data. Its estimate of the unknown function and the associated uncertainties are used to build a so-called acquisition function which does a tradeoff between exploitation and exploration. The latter is then iteratively maximised to find candidates which are promising to be close to the optimum. In this paper, an alternative version of Bayesian optimisation, where the Gaussian process model is replaced by a neural network model, is proposed. As shown in the numerical illustration of this paper, this alternative version will require less computation time when facing optimisation problems with initially large data sets. Since neural networks do not naturally provide an information about the quality of the estimates, a different strategy for the exploration objective of our approach is proposed.

Keywords: Optimization, Output feedback, Optimal control
Abstract: In current practice, once the initial design of an aircraft is completed, it is often revised based on preliminary control synthesis attempts, creating a back-and-fourth iteration for the refinement of the airframe and the control laws. However, joint optimization of the structure and the controller would be advantageous. In view of this, the present paper proposes a technique for simultaneously tuning the airframe and controller parameters of a flexible aircraft. Specifically, a method is provided for the co-design of the flutter suppression control law and the size of control surfaces of the mini MUTT (Multi Utility Technology Testbed) aircraft. To achieve this, static output feedback is used, and the model of the aircraft is parameterized according to the chord length of the flaps. The flutter suppression problem is articulated as the minimization of a nonlinear cost function that considers the energy in the pitching motion of the aircraft, the control effort, and the weight of the actuators. The assessment of the resulting optimal closed-loop proves that the co-design of structural and control parameters is feasible and converges more efficiently to the global optimum in finite steps than the traditional iterative method.

Keywords: Optimization, Delay systems, Optimization algorithms
Abstract: In this paper, we study the so-called two-time-scale gradient descent-ascent method for solving min-max optimization problem. Our focus is to characterize the performance of this method, in particular, its continuous-time variant, under delays in gradient computation. Delays are common issues in large-scale optimization problems, which if not properly addressed, can lead to the instability of gradient methods. Unlike the classic gradient methods where theoretical guarantees for their performance under delays are well-studied, similar results for the gradient descent-ascent algorithms are very sparse. To address this gap, we provide a new analysis to characterize the convergence rates of the two-time-scale gradient descent-ascent dynamics under delays in solving nonconvex min-max optimization under the two-sided Polyak-Lojasiewicz conditions. Our results show that these dynamics converge exponentially to the optimal solution of the problem even under the impact of delays. The key idea in our analysis is to utilize the classic singular perturbation approach to design a coupling Lyapunov function to address the interaction between the gradient descent and ascent dynamics and the effect of delays. Finally, we provide a number of numerical simulations to illustrate our theoretical results.

Keywords: Optimization, Optimal control
Abstract: This paper presents an angular velocity trajectory generation method for a rotary crane boom based on the analysis of skilled operation, which is able to suppress the two-dimensional load-sway consisting of radial and tangential components in a short time by only boom horizontal rotational motion. The proposed trajectory consists of two-stage S-curve profiles for both acceleration and deceleration periods so that the natural period of the load-sway can be changed effectively to achieve the short motion time with load-sway suppression. The proposed trajectory is generated by solving the problem to find minimal motion time considering crane dynamics and constraints on load-sway and machine limitation. The motion performance by the proposed trajectory is compared with the conventional trajectory in simulation considering rope length change and boom-twist typical in a large crane. The proposed trajectory successfully suppresses the load-sway while satisfying the crane constraints in a short time.

Keywords: Autonomous robots, Robotics, Safety critical systems
Abstract: Safe and smooth motion control is essential for mobile robots when performing various automation tasks around obstacles, especially in the presence of people and other mobile robots. The total turning and space used by a mobile robot while moving towards a specified goal position play a crucial role in determining the required control effort and complexity. In this paper, we consider a standard unicycle control approach based on angular feedback linearization and provide an explicit analytical measure for determining the total turning effort during unicycle control in terms of unicycle state and control gains. We show that undesired spiral oscillatory motion around the goal position can be avoided by choosing a higher angular control gain compared to the linear control gain. Accordingly, we establish an accurate, explicit triangular motion range bound on the closed-loop unicycle trajectory using the total turning effort. The improved accuracy in motion range prediction results from a stronger dependency on the unicycle state and control parameters. To compare alternative circular, conic, and triangular motion range prediction approaches, we present an application of the proposed unicycle motion control and motion prediction methods for safe unicycle path following around obstacles in numerical simulations.

Keywords: Autonomous robots, Robotics
Abstract: This paper proposes an algorithm for combined contact detection and state estimation for legged robots. The algorithm models the robot’s movement as a switched system, where different modes relate to different feet being in contact with the ground. The key element of our algorithm is an interacting multiple-model Kalman filter, which identifies the currently-active mode defining contacts, while estimating the state. The rationale for the proposed estimation framework is that contacts (and contact forces) impact the robot’s state, and vice versa. We present validation studies with a quadruped using (i) the high-fidelity simulator Gazebo for a comparison with ground truth values and a baseline estimator, and (ii) hardware experiments with the Unitree A1 robot. The simula- tion study shows that the proposed algorithm outperforms the baseline estimator, which does not simultaneous detect contacts. The hardware experiments showcase the applicability of the proposed algorithm and highlights the ability to detect contacts.

Keywords: Autonomous robots, Automotive, Sensor and signal fusion
Abstract: This paper presents a novel multi-modal Multi-Object Tracking (MOT) algorithm for self-driving cars that combines camera and LiDAR data. Camera frames are processed with a state-of-the-art 3D object detector, whereas classical clustering techniques are used to process LiDAR observations. The proposed MOT algorithm comprises a three-step association process, an Extended Kalman filter for estimating the motion of each detected dynamic obstacle, and a track management phase. The EKF motion model requires the current measured relative position and orientation of the observed object and the longitudinal and angular velocities of the ego vehicle as inputs. Unlike most state-of-the-art multi-modal MOT approaches, the proposed algorithm does not rely on maps or knowledge of the ego global pose. Moreover, it uses a 3D detector exclusively for cameras and is agnostic to the type of LiDAR sensor used. The algorithm is validated both in simulation and with real-world data, with satisfactory results.

Keywords: Autonomous robots, Cooperative control, Robotics
Abstract: The synergy between unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) within a heterogeneous system presents diverse avenues for cooperative operation. These collaborations leverage the complementary strengths of both systems, culminating in remarkable improvements in task execution efficiency. In this paper, we explore the concept of a perception-sensor-deprived UGV, and introduce a ground vehicle guidance and control system that exclusively relies on aerial perception to perform path-following tasks. The cooperative architecture is composed of two parts, one for detecting and tracking the ground trajectory using aerial images retrieved from the UAV. And the second for computing the control inputs for ground autonomous navigation. A nonlinear controller for the ground vehicle is designed using sliding mode with fractional components and its stability analysis is proved using the Lyapunov theory. The cooperative architecture was proved in real-time tests showing that experimental results underscore the well efficacy of this system in autonomously guiding a ground vehicle along a predefined path solely based on aerial imagery, opening new horizons for advanced robotics and autonomous systems. To our best of knowledge, this represents the inaugural endeavor in the development of a car-like robot dynamics controller based on non-stationary aerial imagery for path following utilizing a non-learning approach.

Keywords: Agents and autonomous systems, Cooperative autonomous systems, Linear systems
Abstract: This study develops a control technique, called federated control, to connect disconnected agents in multiagent systems aided by control stations. Specifically, we first use a federated architecture to model multiagent systems with control stations. Based on this architecture, a federated-control procedure is proposed for the task of connecting disconnected agents, together with an initialization procedure. Then, how the federated-control procedure connects disconnected agents by incorporating simple global coordination (i.e., global averaging) is analyzed. We derive the close-form expression of the time sufficient to connect disconnected agents, which possesses interesting properties including early computability and delay independence. The convergence of the formation of multiagent systems and the velocities of all agents can also be proved.

Keywords: Agents and autonomous systems, Cooperative control, Concensus control and estimation
Abstract: This paper considers a variant of the classical cyclic pursuit problem where a multi-agent system (MAS) interacts over a directed cycle on n nodes. The edge weights of the directed cycle are considered to be positive, non-identical real numbers. However, instead of considering a simple directed cycle with these n edges alone, we admit the possibility of self-loops, which renders the resulting cycle digraph non-simple. Within this set-up, we show the effect of self-loops having positive and negative weights on the stability (or lack thereof) of the resulting system dynamics. Since this problem is relevant to the Taylor's model of opinion dynamics, where a positive self-loop weight corresponds to `stubbornness', we also study the effect of such stubborn agents on the final states achieved by each agent in the MAS and show that in presence of a single stubborn agent, consensus results. Though the classical Taylor's model considers interactions over general directed graphs having positive (or binary) edge weights denoting the rate at which an agent attempts to persuade its neighbours, our special set-up over a directed cycle can be used to capture the propagation of opinions over situations such as a telephone game. Finally, we consider the un-weighted version of the directed (non-simple) cycle and present an empirical study related to the effect of positive weighted self-loops in mitigating noise. Simulations are presented to support our results.

Keywords: Stability of nonlinear systems, Chaotic systems, Lyapunov methods
Abstract: The equivalence between local and global characteristics of Lur’e systems is investigated. Historically, such problems date back to Vyshnegradskii’s conjecture on Watt governors and Eden’s conjecture on Lorenz attractors. In the present paper, we develop a unified framework for stability and dimension analyses. This is motivated by the recent works on hidden oscillations and their relations with absolute stability theory. We combine an energy perspective in Lyapunov analysis and a linearization approach in contraction analysis to study global stability and Lyapunov dimension. Notably, we allow a gap between the storage function and the Lyapunov function by utilizing what we call a differential Lyapunov function of the Leonov form. Our framework is also less conservative in the sense that the exact global stability condition and the exact Lyapunov dimension can be characterized. The effectiveness of our method is demonstrated through the Lorenz attractor.

Keywords: Switched systems, Stability of nonlinear systems, Automata
Abstract: This paper studies the construction of symbolic models for switched systems based on a multirate multiscale setting. We focus on switched systems with all incrementally stable subsystems. First, using multiple Lyapunov functions and mode-dependent average dwell-time, sufficient conditions are derived for incremental stability of switched systems, which lays a foundation for the construction of the symbolic models. Second, based on the multirate multiscale setting and bounded dwell-time constraints, multirate multiscale symbolic models are constructed for switched systems. The approximate bisimulation relation is established between the original system and the constructed symbolic model. Finally, the proposed construction method is illustrated via a numerical example from obstacle avoidance problems of robotic systems.

Keywords: Algebraic/geometric methods, Hybrid systems, Nonlinear system theory
Abstract: Hybrid systems are dynamical systems with continuous-time and discrete-time components in their dynamics. When hybrid systems are defined on a principal bundle we are able to define two classes of impacts for the discrete-time transition of the dynamics: interior impacts and exterior impacts. In this paper we define hybrid systems on principal bundles, study the underlying geometry on the switching surface where impacts occur and we find conditions for which both exterior and interior impacts are preserved by the mechanical connection induced in the principal bundle.

Keywords: Control over communication, Hybrid systems, Nonlinear system theory
Abstract: This paper presents a novel event-triggered control (ETC) design framework based on measured L_p norms. We consider a class of systems with finite L_p gain from the network-induced error to a chosen output. The L_p norms of the network-induced error and the chosen output since the last transmission time are used to formulate a class of triggering rules. Based on a small-gain condition, we derive an explicit expression for the L_p gain of the resulting closed-loop systems and present a time-regularization, which can be used to guarantee a lower bound on the inter-transmission times. The proposed framework is based on a different stability- and triggering concept compared to ETC approaches from the literature, and thus may yield new types of dynamical properties for the closed-loop system. However, for specific output choices it can lead to similar triggering rules as "standard" static and dynamic ETC approaches based on input-to-state stability and yields therefore a novel interpretation for some of the existing triggering rules. We illustrate the proposed framework with a numerical example from the literature.

Keywords: Modeling, Mechatronics, Nonlinear system theory
Abstract: Motion stages in high-precision equipment, such as lithography machines, are typically connected to the other parts of the machine. Hysteretic behaviour of such (dynamic) links is detrimental to the tracking performance. In this paper, a quasi-linear frequency-domain approach is presented to analyze the influence of such hysteretic dynamic links on closed-loop tracking performance. The results are demonstrated by means of an industrial relevant numerical example and prove the need for development of compensation schemes for dynamic link-induced disturbances. Moreover, it is shown that hysteresis induced by dynamic links in motion systems primarily affects performance in the low frequency range.

Keywords: Reduced order modeling, Observers for nonlinear systems, Sliding mode control
Abstract: This study presents a new scheme for the synthesis of reduced order nonlinear state observer. It is based on the centre manifold and the sliding mode theories. The first one is well known as being very useful for the generating of reduced order models at the neighborhood of the Hopf bifurcation point while the sliding-mode theory is widely used for the synthesis of nonlinear state observers. Hence , this study investigates the opportunity to derive reduced order state observers from both theories. This way of proceeding obeys to a different spirit regarding that of the Luenberger observer which is based on the estimating of the only states that are not available from measurements while the proposed scheme gives a method to synthesize a reduced order observer from a reduced order model the output of which is forced to be convergent to the measured system output according to the sliding-modes principle. This permits the obtaining of an estimation of the centre variables based on which the estimation of the full order system state is determined.

Keywords: Electrical power systems, Power electronics, LMI's/BMI's/SOS's
Abstract: Phase-locked loop (PLL) algorithms are key elements for the successful integration of converter-interfaced renewable energy sources to the grid. Their main task is to estimate the phase angle of the terminal grid voltage with the aim to keep the converter output current synchronized to it. Yet, due to the increasing penetration of power-electronics-interfaced devices in power systems, the grid voltage signal becomes increasingly disturbed, making the reliable phase estimation a highly demanding task. To address this challenge, we present a robust design method based on matrix inequalities to tune the PLL gains, such that the estimation errors remain bounded in the presence of additive disturbances. Our design approach is formulated as a set of bilinear matrix inequalities (BMIs), which we then propose to solve using the P-K iteration method. This results in a convex problem to be solved at each step. Finally, the benefits of the proposed robust design are illustrated via a numerical example.

Keywords: Electrical power systems, Predictive control for linear systems, Distributed control
Abstract: Microgrids are autonomous clusters of generators, storage units and loads. Special requirements arise in interconnected operation: control schemes that do not require individual microgrids to disclose information about their internal structure and operating objectives are preferred for privacy reasons. Moreover, a safe and economically meaningful operation shall be achieved in presence of uncertain load and weather-dependent availability of renewable infeed. In this paper, we propose a hierarchical distributed model predictive control approach that satisfies these requirements. Specifically, we demonstrate that costs and safety of supply can be improved through a scenario-based stochastic control scheme. In a numerical case study, our approach is compared to a certainty equivalence and a prescient scheme. The results illustrate good performance as well as sufficiently fast convergence.

Keywords: Power electronics, Electrical power systems, Stability of nonlinear systems
Abstract: This paper develops a resilient angle control strategy for inverter-interfaced microgrids subject to magnitude-bounded disturbance. We show that an inverter-interfaced microgrid can be cast as a Lur’e system (a combination of a linear time-invariant system and a nonlinear static state feedback sector-bounded nonlinearity). Then, under this novel description and use of mathematically rigorous input–output stability analysis results, the stability of inverter-based microgrids will be analyzed. The resilience analysis of the microgrids controlled by the proposed angle control approach is based on a mathematically rigorous characterization of disturbances that a microgrid can tolerate so that its stability and operational constraints are not violated. The simulation results evaluate the performance and effectiveness of the proposed resilient angle control over conventional angle droop control methods.

Keywords: Power electronics, Linear parameter-varying systems, Decentralized control
Abstract: This paper proposes a reliability-aware secondary control scheme for power-electronics-dominated DC microgrids with an additional goal of enhancing the microgrid's reliability. The main goal of the paper is to enforce components' reliability, modeled as time-varying parameters, into a reliability-oriented power sharing and voltage balancing. To this end, a DC microgrid under the degradation process of power converters' parameters is modeled by a linear parameter varying (LPV) system. By virtue of this novel description and leveraging tools from stability analysis and control synthesis of LPV systems, as well as insights from the physics of microgrids, the paper develops a novel reliability-oriented distributed secondary control scheme. The proposed scheme does not rely on the topology of microgrids nor the parameters of power lines; it guarantees stability, voltage balancing, and current sharing while taking the reliability aspects and stability constraints into the control design process. Simulation results verify the performance and effectiveness of the proposed secondary control approach.

Keywords: Distributed control, Predictive control for linear systems, Energy systems
Abstract: In this paper, we present a synchronizing DMPC scheme that employs two ingredients: (i) a cost function that penalizes the deviation of the MPC control input from an unconstrained synchronization control law based on algebraic graph theory and (ii) an invariant family of constraints admissible terminal sets for MASs in closed-loop with an unconstrained synchronization control law. We prove that the developed DMPC scheme with a shrinking prediction horizon guarantees finite-time controllability to a family of invariant terminal sets and recursive feasibility. Compared to existing LMI methods for computing a family of constraints admissible invariant sets, we reduce conservatism by exploiting specific graph properties common to MASs. The developed DMPC algorithm for achieving constrained synchronization is tested in different benchmark examples, including balancing capacitor voltages for modular multilevel converters and harmonic oscillators, yielding faster synchronization.

Keywords: Electrical power systems, LMI's/BMI's/SOS's, Reduced order modeling
Abstract: An overall photovoltaic power plant control concept with grid-forming availability without battery storage is proposed. Grid-forming voltage-source converter control is usually studied decoupled from the primary source. Recent studies consider the voltage drop on the DC side for grid-forming control. Until now, the photovoltaic generator and DC-DC converter dynamics are not included. However, it is necessary to consider the overall system dynamics for control design with the objective of fast power control. A generalized control scheme for a two-stage photovoltaic generator model consisting of an aggregated single-diode model of the photovoltaic voltage-controlled current source and a DC-DC converter for tracking the requested power combined with a grid-forming voltage-source converter is presented. For this purpose, the classical maximum power tracking is extended by a generalized demanded tracking. The performance of the approach is evaluated using relevant test cases with active and reactive power requests, grid events like grid voltage drop, phase angle step changes, and changes in the primary resource.

Keywords: Game theoretical methods, Transportation systems
Abstract: When a centrally operated ride-hailing company considers to enter a market already served by another company, it has to make a strategic decision about how to distribute its fleet among different regions in the area. This decision will be influenced by the market share the company can secure and the costs associated with charging the vehicles in each region, all while competing with the company already operating in the area. In this paper, we propose a Colonel Blotto-like game to model this decision-making. For the class of games that we study, we first prove the existence and uniqueness of a Nash Equilibrium. Subsequently, we provide its general characterization and present an algorithm for computing the ones in the feasible set’s interior. Additionally, for a simplified scenario involving two regions, which would correspond to a city area with a downtown and a suburban region, we also provide a method to check for the equilibria on the feasible set's boundary. Finally, through a numerical case study, we illustrate the impact of charging prices on the position of the Nash equilibrium.

Keywords: Traffic control, Electrical power systems, Modeling
Abstract: The impending green transition of the power system, brought about by the spread of Renewable Energy Sources, Energy Storage, and Electric Vehicles (EVs), calls for novel grid management solutions. Techniques using concepts such as Virtual Power Plants and Virtual Power Lines have already successfully been deployed to help manage the grid and shift power transmission in time. In this work, we propose and discuss a similar technique which can potentially match and surpass these results, which we refer to as EV Virtual Power Lines. By controlling the charging station prices and charging rates in a network in which EVs are circulating, we effectively shift the power transmission both in time and in space. This allows us to reinforce and balance the power grid, reducing grid congestion without disrupting EV operation. We model and analyse the dynamics of EVs circulating between two urban nodes in mixed traffic, and design control schemes that achieve charging station power reference tracking. Using a simple simulation example, performance achievable by applying such control schemes is demonstrated.

Keywords: Transportation systems, Traffic control, Energy systems
Abstract: Electric vehicles and the electric vehicle charging station infrastructure play crucial roles in sustainable energy systems. We propose an innovative approach that utilizes aggregated electric vehicles for grid-balancing services in the auxiliary market. Our model gives electric vehicle state-of-charge over time and space, considering factors like driver behavior, state-of-charge levels, and charging/discharging costs. This approach informs decisions about optimal charging times. Charging station operators participate in the frequency containment reserves market in collaboration with aggregators. We introduce an optimization framework which establishes pricing strategies to maximize profits for aggregators and charging station operators while minimizing charging costs for electric vehicle users. Our findings demonstrate the effectiveness of this strategy in realistic simulations, integrating electric vehicle mobility and the electricity frequency containment reserves market.

Keywords: Transportation systems, Optimization, Control over communication
Abstract: Electric trucks usually need to charge their batteries during long-range delivery missions, and the charging times are often nontrivial. As charging resources are limited, waiting times for some trucks can be prolonged at certain stations. To facilitate the efficient operation of electric trucks, we propose a distributed charging coordination framework. Within the scheme, the charging stations provide waiting estimates to incoming trucks upon request and assign charging ports according to the first-come, first-served rule. Based on the updated information, the individual trucks compute where and how long to charge whenever approaching a charging station in order to complete their delivery missions timely and cost-effectively. We perform empirical studies for trucks traveling over the Swedish road network and compare our scheme with the one where charging plans are computed offline, assuming unlimited charging facilities. It is shown that the proposed scheme outperforms the offline approach at the expense of little communication overhead.

Keywords: UAV's, Intelligent systems, Energy systems
Abstract: Traffic air congestion should be considered in future deployments of Low-Altitude Air city Transport (LAAT) systems. In addition to the congestion concerns, the low-altitude aircraft is being designed with limited energy capacity due to design constraints and battery technologies. Hence, energy consumption concerns should also be considered within LAAT operations. This paper examines the energy consumption of low-altitude aircraft in air mobility (AM) operations, intending to improve the environmental impact of air mobility in urban and regional areas. To achieve this, the study enhances the LAAT model-based operational framework by integrating an energy consumption model (ECM) for low-altitude aircraft. The framework couples modeling and control of microscopic and macroscopic levels of AM operations. Including the ECM allows us to explore the relationship between macroscopic energy consumption and known macroscopic traffic flow variables. As a result, this paper contributes to the literature with the development of the LAAT Energy Consumption Model (LeM). The LeM does not only quantify the energy consumption of individual aircraft but also facilitates the aggregation of energy consumption for the entire airspace. The study realizes LeM with a simplified feedback control design, i.e., a gating policy, which optimizes the number of aircraft allowed into the network, balancing energy efficiency and traffic efficiency in LAAT networks. The development of the LeM provides a valuable tool for assessing the environmental impact of LAAT systems. LeM can be a benchmark to diagnose airspace conditions and enhance traffic control strategies for operating efficiently and sustainably of LAAT systems.

Keywords: Traffic control, Transportation systems, Behavioural systems
Abstract: Urban traffic congestion remains a pressing challenge in our rapidly expanding cities, despite the abundance of available data and the efforts of policymakers. By leveraging behavioral system theory and data-driven control, this paper exploits the Data-enabled Predictive Control (DeePC) algorithm in the context of urban traffic control performed via dynamic traffic lights. To validate our approach, we consider a high-fidelity case study using the state-of-the-art simulation software package Simulation of Urban MObility (SUMO). Preliminary results indicate that DeePC outperforms existing approaches across various key metrics, including travel time and CO2 emissions, demonstrating its potential for effective traffic management.

Keywords: Agents networks, Control over networks, Cooperative control
Abstract: In this paper, the output synchronization in large-scale discrete-time networks is examined by utilizing the novel phase tool, where the agent dynamics are allowed to be significantly heterogeneous. The synchronization synthesis problem is formulated and thoroughly investigated, with the goal of characterizing the allowable heterogeneity among the agents to ensure synchronization under a uniform controller. The solvability condition is provided in terms of the phases of the residue matrices of the agents at the persistent modes. When the condition is satisfied, a design procedure is given, producing a low-gain synchronizing controller. Numerical examples are given to illustrate the results.

Keywords: Game theoretical methods, Sensor and mesh networks, Nonlinear system theory
Abstract: Sensor network localization (SNL) problems require determining the physical coordinates of all sensors in a network. This process relies on the global coordinates of anchors and the available measurements between non-anchor and anchor nodes. Attributed to the intrinsic non-convexity, obtaining a globally optimal solution to SNL is challenging, as well as implementing corresponding algorithms. In this paper, we formulate a non-convex multi-player potential game for a generic SNL problem to investigate the identification condition of the global Nash equilibrium (NE) therein, where the global NE represents the global solution of SNL. We employ canonical duality theory to transform the non-convex game into a complementary dual problem. Then we develop a conjugation-based algorithm to compute the stationary points of the complementary dual problem. On this basis, we show an identification condition of the global NE: the stationary point of the proposed algorithm satisfies a duality relation. Finally, simulation results are provided to validate the effectiveness of the theoretical results.

Keywords: Behavioural systems, Network analysis and control, Large-scale systems
Abstract: Networks of dynamical systems play an important role in various domains and have motivated many studies on the control and analysis of linear dynamical networks. For linear network models considered in these studies, it is typically pre-determined what signal channels are inputs and what are outputs. These models do not capture the practical need to incorporate different experimental situations, where different selections of input and output channels are applied to the same network. Moreover, a unified view of different network models is lacking. This work makes an initial step towards addressing the above issues by taking a behavioral perspective, where input and output channels are not pre-determined. The focus of this work is on behavioral network models with only external variables. By exploiting the concept of hypergraphs, novel dual graphical representations, called system graphs and signal graphs, are introduced for behavioral networks. Moreover, connections between behavioral network models and structural vector autoregressive models are established. In addition to their connections in graphical representations, it is shown that the regularity of interconnections is an essential assumption when choosing a structural vector autoregressive model.

Keywords: Biological systems, Stability of linear systems, Network analysis and control
Abstract: Complex ecological networks are often characterized by intricate interactions that extend beyond pairwise relationships. Understanding the stability of higher-order ecological networks is salient for species coexistence, biodiversity, and community persistence. In this article, we present complexity analyses for determining the linear stability of higher-order ecological networks through tensor decompositions. We are interested in the higher-order generalized Lotka-Volterra model, which captures high-order interactions using tensors of varying orders. To efficiently compute Jacobian matrices and thus determine stability in large ecological networks, we exploit various tensor decompositions, including higher-order singular value decomposition, Canonical Polyadic decomposition, and tensor train decomposition, accompanied by in-depth computational and memory complexity analyses. We demonstrate the effectiveness of our framework with numerical examples.

Keywords: Computer networks, Network analysis and control, Large-scale systems
Abstract: We conduct a local stability analysis of a class of Internet congestion control protocols, approaching the problem from a novel entangled gain and phase perspective. Our approach incorporates a recently revitalized quantitative MIMO phase concept, which we use to reexamine the local version of certain classical stability results. This phase information is entangled with the gain information through the concept of the Davis-Wielandt shell, providing an intuitive graphical interpretation and reducing the conservatism of stability conditions. Leveraging these tools, we derive decentralized stability conditions for the Internet protocols under consideration.

Keywords: Agents networks, Control over networks, Large-scale systems
Abstract: A problem with considering correlations in the analysis of multiagent system with stochastic packet loss is that they induce dependencies between agents that are otherwise decoupled, preventing the application of decomposition methods required for efficient evaluation. To circumvent that issue, this paper is proposing an approach based on analysing sets of networks with independent communication links, only considering the correlations in an implicit fashion. Combining ideas from the robust stabilization of Markov jump linear systems with recently proposed techniques for analysing packet loss in multiagent systems, we obtain a linear matrix inequality based stability condition which is independent of the number of agents. The main result is that the set of stabilized probability distributions has non-empty interior such that small correlations cannot lead to instability, even though only distributions of independent links were analysed. Moreover, two examples are provided to demonstrate the applicability of the results to practically relevant scenarios.

Keywords: Energy systems, Process control, Predictive control for linear systems
Abstract: Stochastic predictive control schemes that account for epistemic and aleatoric uncertainties, i.e. lack of model knowledge and stochastic disturbances, are of major interest for multi-energy systems. However, there exists a trade-off between model complexity, computational effort, and accuracy of uncertainty quantification. This paper attempts to assess this trade-off by comparing a recently proposed approach combining Willems’ fundamental lemma with polynomial chaos expansion to a model-based scheme that first propagates uncertainty with PCE and then considers chance constraints in the optimization. The simulation results show that the data-driven scheme yields similar performance and computational efficiency compared to the model-based scheme, with the advantage of avoiding the construction of explicit models.

Keywords: Identification for control, H2/H-infinity methods, LMI's/BMI's/SOS's
Abstract: This paper deals with the data-driven synthesis of dissipative linear systems in discrete time. We collect finitely many noisy data samples with which we synthesise a controller that makes all systems that explain the data dissipative with respect to a given quadratic supply rate. By adopting the informativity approach, we introduce the notion of informativity for closed-loop dissipativity. Under certain assumptions on the noise and the system, with the help of tools for quadratic matrix inequalities, we provide necessary and sufficient conditions for informativity for closed-loop dissipativity. We also provide a recipe to design suitable controllers by means of data-based linear matrix inequalities. This main result comprises two parts, to account for both the cases that the output matrices are known or unknown. Lastly, we illustrate our findings with an example, for which we want to design a data-driven controller achieving (strict) passivity.

Keywords: Iterative learning control, Distributed parameter systems, Identification for control
Abstract: The goal of this paper is to make a strong point for the usage of dynamical models when using reinforcement learning (RL) for feedback control of dynamical systems governed by partial differential equations (PDEs). To breach the gap between the immense promises we see in RL and the applicability in complex engineering systems, the main challenges are the massive requirements in terms of the training data, as well as the lack of performance guarantees. We present a solution for the first issue using a data-driven surrogate model in the form of a convolutional Long-Short Term Memory network with actuation. We demonstrate that learning an actuated model in parallel to training the RL agent significantly reduces the total amount of required data sampled from the real system. Furthermore, we show that iteratively updating the model is of major importance to avoid biases in the RL training. Detailed ablation studies reveal the most important ingredients of the modeling process. We use the chaotic Kuramoto-Sivashinsky equation do demonstrate our findings.

Keywords: Robotics, Neural networks, Modeling
Abstract: As new soft robotic applications emerge, control requirements increase. Therefore, precise control methods for soft robots are needed. The main challenge in controlling soft robots is that soft robots are often underactuated and redundantly actuated at the same time. In addition, modeling is usually difficult due to large elastic deformations, unknown material parameters, and manufacturing inaccuracies. In soft robotics, so-called kinematic controllers, which neglect the dynamics of the system, are mainly used. In particular, datadriven controllers are very popular. However, more advanced applications of soft robots require increasingly faster and more accurate movements. Here, kinematic controllers are not sufficient anymore. A direct extension of existing data-driven kinematic controllers to dynamic control is usually not practical due to the huge amount of training data required. This paper presents a new open-loop dynamic trajectory tracking control of a redundantly actuated soft robot. A combination of a kinematic data-driven controller based on neural networks and a dynamic model-based control approach based on model inversion with the servo-constraints approach is used. This combined approach preserves the advantages of learning-based kinematic controllers for the dynamic control of soft robots while keeping the amount of training data required low. Experimental results show the strength of this approach.

Keywords: Sampled data control, Predictive control for nonlinear systems, Output regulation
Abstract: We propose a model predictive control (MPC) scheme with sampled-data input which ensures output-reference tracking within prescribed error bounds for relative-degree-one systems. Hereby, we explicitly deduce bounds on the required maximal control input and sampling frequency such that the MPC scheme is both initially and recursively feasible. A key feature of the proposed approach is that neither terminal conditions nor a sufficiently-large prediction horizon are imposed, rendering the MPC scheme computationally efficient. We illustrate the MPC algorithm via a numerical example of a torsional oscillator.

Keywords: Network analysis and control, Feedback linearization, Observers for nonlinear systems
Abstract: Analysis and control of network systems largely rely on the availability of the network topology and the governing dynamics. In some cases, where the network dynamics and topology may not be available, researchers have relied on data-driven methods for the control of networks. A strict requirement of these methods is the collection of large amounts of persistently exciting data to enable control design. Moreover, these methods are largely restricted to linear systems and due to the open-loop nature of the control, lack robustness in the presence of external disturbances. In order to overcome these limitations, we present an online data-driven closed-loop control architecture for a nonlinear network system with unknown topology. Our method uses a novel ‘cut-and-rewire’ technique to assign a network topology that meets the desired control objectives while obviating the need for persistently exciting inputs and large open-loop offline data collection. We provide local asymptotic (exponential) stability guarantees for the closed-loop dynamics. We validate the results on a network of Kuramoto oscillators and achieve synchronization, phase balancing, and cluster formation when the underlying oscillator network topology is unknown and with noisy measurements.

Keywords: Robust control, Differential algebraic systems, Lyapunov methods
Abstract: The study of recent papers brought our attention to a alternative matrix inequality condition for state-feedback design. This condition falls in the category of S-variable results. At the difference of previous conditions is does not involve Schur complement or duality arguments and thus simplifies significantly the mathematical derivations. We provide in this paper a detailed description of the core elements of this new to us result. We then expose some derivations for pole location and time-response performances of uncertain systems with constant or time-varying uncertainties, as well as for some non-linear systems.

Keywords: Robust control, Markov processes, Constrained control
Abstract: In this paper, we investigate the control of a cyber-physical system (CPS) while accounting for its vulnerability to external attacks. We formulate a constrained stochastic problem with a robust constraint to ensure robust operation against potential attacks. We seek to minimize the expected cost subject to a constraint limiting the worst-case expected damage an attacker can impose on the CPS. We present a dynamic programming decomposition to compute the optimal control strategy in this robust-constrained formulation and prove its recursive feasibility. We also illustrate the utility of our results by applying them to a numerical simulation.

Keywords: Robust control, Optimization, Optimization algorithms
Abstract: We consider a constrained Markov decision process with uncertain transition probabilities, where the uncertainty is driven by a single parameter which belongs to an interval. We model it using a robust optimization framework and show that it is equivalent to a bilinear programming problem. We propose a linear programming- based algorithm to compute its global optimal solution. The numerical experiments are performed on a well-known class of Markov decision problems called Garnets using our algorithm as well as Gurobi bilinear solver. We observe that for the case of dense transition probabilities, our algorithm outperforms Gurobi bilinear solver.

Keywords: Robust control, Uncertain systems, Linear parameter-varying systems
Abstract: This paper presents the design of a dissipativity-based output feedback control for transient performance improvement in linear time-varying discrete-time systems. The uncertain system dynamics is described in a convex polytope, and is subjected to bounded external disturbances. The proposed reduced-order time-varying dynamic controller guarantees that the system trajectories are bounded below a specified threshold for a given finite time interval. Moreover, using the notion of QSR-dissipativity, we aim for the system to simultaneously satisfy dissipativity to external disturbances within the considered finite time interval. Sufficient difference linear matrix inequality (DLMI) conditions are derived to design output feedback finite-time dissipative controller gains, considering the augmented form of the closed-loop system-controller dynamics. The paper unifies the procedure of designing finite-time robust controllers, as performance indices such as finite-time passivity and finite L2-gain are special cases of finite-time dissipativity. Numerical simulations demonstrate the effectiveness of the proposed scheme in bounding the state trajectories within the prescribed limit, for a specified finite time interval.

Keywords: Robust control
Abstract: The incremental stability analysis of Lurie systems consisting of the feedback interconnection between a linear time-invariant (LTI) system and a slope-bounded nonlinearity is considered. We first show that the incremental input-output mappings generated by the set of slope-bounded nonlinearities satisfy a set of biased integral quadratic constraints (IQCs) defined by Popov multipliers. Then, a frequency-domain inequality (FDI) condition on the LTI system is proposed for establishing incremental closed-loop stability via an incremental form of IQC theory. Application of the KYP lemma yields an equivalent linear matrix inequality (LMI) condition.

Keywords: Robust control, Optimal control, H2/H-infinity methods
Abstract: Application of Robust Control Toolbox for Time Delay Systems implemented in the Matlab system to the oscillating plant with uncertain time delay and astatism using the D-K iteration and algebraic approach. The algebraic approach combines the structured singular value, algebraic theory and algorithm of global optimization solving remaining issues in structured singular value framework. The algorithm for global optimization can be alternated with direct search methods such as Nelder-Mead simplex method giving solutions for problems with one local extreme. As a global optimization method, Differential Migration is used proving to be reliable in solving this type of problems. The D-K iteration represents a standard method in the structured singular value theory. The results obtained from the D-K iteration are compared with the algebraic approach.

Keywords: Biomedical systems, Modeling
Abstract: Target-controlled infusion (TCI) constitutes a clinically available alternative to manually administering the infusion rate of the anesthetic drug propofol. In TCI, a drug infusion profile is optimized to track a reference trajectory of blood plasma or effect site (brain cortex) drug concentration, or a corresponding clinical effect. TCI is a pure feed-forward open-loop strategy, fully reliant on an underlying dynamic patient model. We show how TCI dosing of propofol to achieve a desired depth of hypnosis-can be posed as a QP problem. We design this QP problem based on a nominal pharmacological propofol model by Eleveld et al. Then, we investigate how inter-patient variability, described as mixed effects of a particular distribution within the Eleveld model, affects TCI performance. Based on the Mahalanobis distance, we sample from probability quantiles of the mixed-effect model and evaluate the TCI designed for the nominal patient across these samples. The main outcome is that performance, in terms of achieved hypnotic depth, deteriorates to what is at the limit of clinical acceptance already when considering only 1 % of the most likely patients drawn from the uncertainty model. This is under the-for the TCI system unrealistically favorable-assumption that there is no uncertainty in the relation between effect site concentration, and clinical effect. The conclusion from the arising results is that the benefit of propofol TCI over manual dosing is unclear, even within the model that the TCI system was designed for.

Keywords: Modeling, Biomedical systems
Abstract: In personalized medicine applications such as general anesthesia, an individualised pharmacokinetic (PK) model requires to move away from the classical assumption of homogeneous drug mixing in various tissue compartments in the body. By default, these model coefficients are pre-surgery initialized from population based models as a function of age, gender, weight, height, lean body mass and do not include at this moment specific drug diffusion patterns in the fat compartment, whereas various types of fat will induce various time constants in non-lean patients. In this work, the pharmacokinetic compartmental model structure is revisited to account for non-uniform distribution of uptake/clearance time constants in patients as a nonlinear function of body mass index. Simulations are confirming expected patterns of drug distribution in the body and can account for post-anesthesia side effects up to 72 hours. The model is a novel advance in providing the control community with yet increasingly realistic patient models for closed loop control of anesthesia.

Keywords: Biomedical systems, Robust control, Decentralized control
Abstract: Monitoring and control of general anesthesia, involving cardiac output or mean arterial pressure are critical to ensure patient safety during surgery. Several computer control solutions have been developed for each of the anesthesia components and hemodynamic processes. However, most do not tackle the synergistic and antagonistic effects of the anesthetic and hemodynamic drugs. Hitherto, only a handful of preliminary results and ideas regarding multivariable control have been reported so far, usually considering a simplified decentralized approach. A decoupled control strategy is proposed here to reduce the interaction between the hemodynamic and anesthesia sub-systems, hence increasing the robustness and stability of the overall control loop. Due to their instrinisc robustness to uncertainty and process model variability, fractional order controllers are designed to ensure that more specific performance criteria are addressed, compared to the traditional PIDs. The decoupled control strategy is compared to the decentralized approach to validate the minimization of the interactions. A robustness analysis is performed using a benchmark patient model and data from 24 patients.

Keywords: Biomedical systems
Abstract: This study aims to compare the effectiveness of different Pharmacokinetic-Pharmacodynamic (PK-PD) models for administering Propofol and Remifentanil, two critical agents in anesthesia. Initially, different PK models were introduced: one for Propofol based on the Schnider model and another for Remifentanil using the Minto model. Alternatively, both drugs were modeled using the Eleveld models. The PK-PD models were integrated into a closed-loop control system using model predictive control (MPC) with disturbances to control the Bispectral index (BIS) and the Richmond Agitation Sedation Scale (RASS). The methodology involved simulating the anesthetic agents in the open-source patient simulator (2 inputs, 2 outputs) with 12 patient datasets in a controlled environment to simulate the patient response variability, allowing for a detailed analysis of the model's performance in maintaining optimal drug concentrations. The primary focus was on the system's ability to adapt to surgical disturbances, a key challenge in anesthesia management, and whether a different modeling of drugs can have an impact on their effects. The results indicated significant differences in the performance of the two models configurations. The Eleveld model for Propofol showed less usage of drugs to maintain the desired BIS value. Concluding that this comparative analysis offers a valuable reference for selecting appropriate modeling approaches in the development of advanced control strategies in anesthesia.

Keywords: Machine learning
Abstract: Control performance decreases significantly in the presence of uncertainty in variable availability, measurement noise, or instrumentation failure. In cluttered environments such as the Post Anesthesia Care Unit (PACU), clinical measures are often obtained intermittently and influenced by noise and artifacts. An important component in post-operative treatment is the assessment and management of pain levels. However, reliable information is critical for clinically relevant results and improved patient outcomes. From a control engineering point of view, variables are often estimated and interpolated to allow a suitable flow of feedback information to control loops for the optimization of drug dosages. In this context, Artificial Intelligence (AI) stands as a promising tool to augment pain level assessment. This study introduces and compares two distinct AI-based approaches for predicting continuous Numerical Rating Scales (NRS) based on heart rate (HR) data. The first approach uses polynomial regression, lasso regression, and ridge regression, while the second employs an LSTM model. Notably, the AI prediction model, independent of traditional interpolation techniques, outperforms the approach relying on interpolation. This showcases a more frequent and precise assessment of pain levels. The proposed AI-based method holds promise for continuous estimation and can serve as an estimator for model-based control, optimizing pain management in post- operative settings. This proof of concept study underscores the potential of AI tools to significantly enhance pain level assessment

Keywords: LMI's/BMI's/SOS's, Linear parameter-varying systems
Abstract: Various pathologies and physical impairments diminish the capacity to maintain a seated balance, with spinal cord injury serving as a clinical example. Individuals with this condition often lose control of muscles below the injury level and commonly rely on a wheelchair for mobility. The impact of such injuries on seated postural control necessitates the development of new stabilization strategies in response to disturbances. These strategies differ significantly from those used by asymptomatic individuals. Particularly, they rely on upper limb movements as the primary means of control, given the absence of control from the trunk. Reconstructing the produced active joint torques at the shoulder and arm levels or the passive torque at the lumbo-sacral level is crucial to understand compensatory strategies and developing innovative monitoring techniques in rehabilitation exercises. The methodology starts from a nonlinear model with 5 Degrees of Freedom called “Trunk-2-Arms” (T2A) and proposes an observer based on quasi-LPV and LMI problem synthesis. The design of the observer uses a cascade of 3 models (trunk and the 2 arms) in order to reduce the design complexity. Therefore, local PI-observers are derived that allows to estimate the state and the human joint torques. The global estimation error convergence of the cascaded observer scheme is guaranteed using a separation principle and the Lyapunov theory. The methodology is validated through simulations and using real clinical data collected on 26 SCI people.

Keywords: Identification for hybrid systems, Fault estimation, Switched systems
Abstract: This paper studies the problem of secure state estimation of networked switched systems in the presence of denial-of-service (DoS) attacks, as well as disturbances and measurement noise. Firstly, a state transformation rule is designed to partition the original system into two subsystems, facilitating the design of discrete and continuous state observers. Secondly, by modifying the traditional super-twisting sliding-mode method and taking into account the frequency and duration characteristics of DoS attacks, we employ dynamic differential properties between different modes to design a switching law identification strategy. We show that this strategy can accurately estimate the switching state without imposing any requirement on the switching times and sequences. Thirdly, based on the identified activated mode, a set of mode-dependent continuous state sliding-mode observers is designed, that achieves continuous state estimation in finite time. The practicality and applicability of the developed results are validated through numerical simulations.

Keywords: Observers for nonlinear systems, Safety critical systems, Energy systems
Abstract: We consider the problem of estimating the states of a continuous-time nonlinear dynamical system, where a subset of sensors can be maliciously corrupted using a potentially unbounded additive signal. The proposed estimation scheme employs a bank of observers which are robust with respect to disturbances and attacks, in conjunction with median operation to build the state estimate. The median operation is the key ingredient which guarantees that the state estimate is constructed using sensor(s) which are not under attack. A standing assumption in this scheme is that the system has to be observable from each sensor. We provide a constructive design method for the state observers for a class of nonlinear systems and illustrate the efficacy of the resilient median-based state estimation scheme using real data on an inverter-based energy distribution network.

Keywords: Safety critical systems, Fault detection and identification
Abstract: Safety filters ensure that only safe control actions are executed. We propose a simple and stealthy false-data injection attack for deactivating such safety filters; in particular, we focus on deactivating safety filters that are based on control-barrier functions. The attack injects false sensor measurements to bias state estimates to the interior of a safety region, which makes the safety filter accept unsafe control actions. To detect such attacks, we also propose a detector that detects biases manufactured by the proposed attack policy, which complements conventional detectors when safety filters are used. The proposed attack policy and detector are illustrated on a double integrator example.

Keywords: Electrical power systems, Control over networks, Stability of nonlinear systems
Abstract: Electric grids with a high share of inverter-interfaced power sources require novel control approaches like the popular Virtual Synchronous Machine (VSM) to ensure stable operation. Such power systems are increasingly relying on real time communications between individual machines to achieve control objectives. Signals sent over a communication network include power set points, configuration parameters and machine health data. A novel damping mechanism for VSM, Virtual Friction (VF), makes use of this communication infrastructure to provide damping for the machines with low impact on the output powers of the inverters during frequency deviations from the nominal. We investigate in this paper, how this system can be secured when the communication channels are compromised by malicious actors. We analyze a microgrid with several VSMs employing VF in the presence of manipulated signals, and a secure control scheme is proposed that is able to maintain strong damping during attacks. The efficacy of the proposed secure control scheme is validated using a high fidelity simulation model.

Keywords: Agents and autonomous systems, Fault detection and identification, Fault tolerant systems
Abstract: We consider the problem of average consensus in a distributed system comprising a set of nodes that can exchange information among themselves. We focus on a class of algorithms for solving such a problem whereby each node maintains a state and updates it iteratively as a linear combination of the states maintained by its in-neighbors, i.e., nodes from which it receives information directly. Averaging algorithms within this class can be thought of as discrete-time linear time-varying systems without external driving inputs and whose state matrix is column stochastic. As a result, the algorithms exhibit a global invariance property in that the sum of the state variables remains constant at all times. In this paper, we report on another invariance property for the aforementioned class of averaging algorithms. This property is local to each node and reflects the conservation of certain quantities capturing an aggregate of all the values received by a node from its in-neighbors and all the values sent by said node to its out-neighbors (i.e., nodes to which it sends information directly) throughout the execution of the averaging algorithm. We show how this newly-discovered invariant can be leveraged for detecting errors while executing the averaging algorithm.

Keywords: Fault detection and identification, Fault diagnosis, Robust adaptive control
Abstract: This paper proposes an adaptive-observer-based threat discrimination method for systems with disturbances, aiming to identify the occurring threat type: component faults or cyber attacks. Stealthy attacks are typically exponentially decaying with time and only slightly alter the system outputs, the effects of which can be easily inundated by non-attacker-incurred disturbances. To this end, an integrator is applied to the system output to retain the effects of stealthy attacks. Compared to the classical integral-type adaptive observers with a constant adaptation gain, a dynamic adaptation gain is exploited to provide additional degrees of design freedom for frequency-domain loop-shaping. This allows to apply distinct threat/disturbance-to-residual gains in the frequency intervals to which the threats and the disturbances belong, respectively, thereby improving the threat discrimination performance. A numerical example to demonstrate the effectiveness of the proposed methodology is presented.

Keywords: Transportation systems, Traffic control
Abstract: This tutorial talk is motivated by the possibility of a small number of automated vehicles (AVs) that may soon be present on our roadways, and the impacts they will have on traffic flow. This automation may take the form of fully autonomous vehicles without human intervention (SAE Level 5) or, as is already the case in many modern vehicles, may take the form of driver assist features such as adaptive cruise control (ACC) or other SAE Level 1 and 2 features. Regardless of the extent of automation, the introduction of such vehicles has the potential to substantially alter emergent properties of the flow while also providing new opportunities for control of the traffic flow. However, AVs and automation features may initially be quite costly and restricted only to a small number of road users, thus restricting the benefit to only those who can afford AVs. Instead, growing research has suggested that AVs can be used to improve traffic flow conditions for all road users, even those who do not use AV technology. In this talk, I review recent work that suggests how AVs in the traffic flow may alter traffic dynamics. Next, I will review a few traffic flow control techniques (e.g., ramp metering), and discuss how they can be modified to be socially equitable in a mixed-autonomy setting, while still relying on legacy infrastructure. This tutorial reviews how vehicle automation may impact traffic dynamics, and highlights recent work on how to close the loop with new traffic controllers that leverage these flow dynamics and equitably reduce travel time for all road users.

Keywords: Transportation systems, Optimization, Game theoretical methods
Abstract: This talk discusses the challenges that mobility systems can face due to the presence of self-interested users and myopic societal objectives, and presents potential solutions arising from the adoption of accessibility fairness metrics and equitable tolling schemes based on artificial currencies. Specifically, I will first give an overview of our research activities on the operation of intermodal mobility systems, where users can complete a trip using different means of transportation, such as autonomous cars, public transit and active modes. Thereby, I will discuss potential inefficiencies resulting from users’ selfish behavior, whilst also reflecting on current narratives and paradigms. Second, I will introduce incentive mechanisms to address these inefficiencies with an artificial currency that cannot be bought or traded, but only spent or received when traveling. Assuming the users to be rational, I will demonstrate how such schemes can achieve near-optimal routing whilst significantly reducing the users’ perceived discomfort when compared to a centralized optimal allocation that does not consider user urgency.

Keywords: Transportation systems, Optimal control, Traffic control
Abstract: Global urbanization and burgeoning urban populations impose several societal challenges associated with disparities in transportation opportunities, reduced accessibility to essential services for marginalized communities, and increased social isolation due to lengthy commutes. Although emerging mobility systems, e.g., connected and automated vehicles (CAVs), and shared mobility, provide the most intriguing opportunity to mitigate these challenges, research efforts have focused mainly on optimizing their operation efficiency in isolation without deliberating on human acceptance and perception. Addressing the societal issues inherent in emerging mobility systems remains largely uncharted territory. The core of these societal challenges lies in the unequal distribution of transportation modes and access to urban resources, giving rise to "mobility equity." While mobility equity has been studied from multiple perspectives, including socioeconomic parity, equitable spatial infrastructure allocation, and alignment of resource distribution with societal needs, a critical gap remains in integrating mobility equity principles into emerging transportation modes. In this talk, I will present a mobility equity metric (MEM) to quantify the accessibility and fairness in a transportation network consisting of CAVs and human-driven vehicles. I will then present a routing framework integrated with MEM that aims to distribute travel demand for the transportation network, resulting in a socially optimal mobility system. A “socially optimal mobility system” is defined to be a mobility system that (1) is efficient in terms of travel time, (2) improves accessibility, and (3) ensures equity in transportation. To accommodate compliant and noncompliant vehicles to the routing suggestions, the framework incorporates a cognitive hierarchy model commonly used in behavioral economics to predict human decisions in transportation systems. The proposed framework aims to bolster mobility equity by

Keywords: Transportation systems, Traffic control, Cooperative control
Abstract: In a transportation network consisting entirely of Connected Automated Vehicles (CAVs), it has been shown how to design effective cooperative strategies to jointly minimize time, energy, and comfort while guaranteeing safety in different conflict areas of the network so as to achieve safe and socially optimal operation. In the presence of mixed traffic, where CAVs must interact with Human-Driven Vehicles (HDVs), achieving such social optimality under safety guarantees is much more challenging. We will present recent developments showing that in many cases a small number of CAVs can form “coalitions” through which they can eliminate or greatly reduce the interaction between CAVs and HDVs and often derive safe and socially optimal CAV trajectories independent of HDV behavior. When HDV behavior needs to be explicitly modeled, we address the question: “when human drivers make decisions to cooperate or not in specific conflict situations (e.g., merging decisions), how can we ensure that they are cooperation compliant?” We propose a general framework for such cooperation compliance that avoids explicit incentives or pricing schemes.

Keywords: Control education
Abstract: The peer review process plays an essential role in ensuring the quality of scientific works and providing a measure of trust. Independent reviewers evaluate the quality of submitted manuscripts and utilize a suggestive feedback mechanism for possible improvements. Owing to the significant importance of peer review in the control community, in this event, we have gathered a panel composed of the EUCA President, Editorial Chairs, and experienced and young scholars to provide information on the current peer-review activities, ranging from performance to involvement, and discuss some of the emerging trends in peer review, such as diversity, biases, and the use of AI.

Panelists/speakers: Antonella Ferrara (University of Pavia) Umar Niazi (MIT) Thomas Parisini (University of Trieste, Imperial College) Jana Tumova (KTH) Maria Elena Valcher (University of Padova)

Keywords: Control education
Abstract: The teaching of autonomous vehicle (AV) control algorithm design involves the drawing together of multiple topics from both theory and practice. To enable an in-depth knowledge of how the control algorithms of an AV operate, a laboratory-scale approach using ‘lab-in-a-box’ has been developed at Aston University for a project-based module on the Future Vehicle Technologies MSc course. The aim of the approach is such that students can learn the control engineering fundamentals before moving onto a larger vehicle platform. Using MATLAB and Simulink, the adopted approach borrows ideas inspired from model-based design for embedded control. Continuous-time and discrete-time simulation tools are used to design what is in effect a real-time control system. Students are also taught ‘model shop’ skills and computer aided design (CAD), thus allowing them to build a physical control system for real-time operation. The focus is on the control of a DC motor, including speed control, temperature control and obstacle avoidance. The learning from the ‘lab-in-a-box’ then allows for the skills to be transferred to a scaled down vehicle, with the Roboworks Rosbot Plus TX platform used. The feedback from students is overall very positive, with the students enjoying the transferrable skills from the ‘lab-in-a-box’ to the Roboworks Rosbot Plus TX platform.

Panelists/speakers: Dr. James E. Pickering, Lecturer of Control, Mechatronics and Autonomous Systems at Aston University, UK

Keywords: Feedback linearization
Abstract: The feedback linearization problem determines a change of state and input coordinates that reduces nonlinear control systems to linear ones, considerably simplifying the design of controllers. Being based on geometric conditions, the feedback linearization problem is hard to solve when the system's model is unknown or uncertain. In this talk we discuss a new method that can learn the change of coordinates from a library of candidate functions. When the dynamics of the system are known, the method reduces to solving a set of linear equations. Remarkably, the same idea extends to the case in which the dynamics are unknown and a solution must be found using a finite number of experimental data.

Keywords: Emerging control applications
Abstract: Microcontrollers play a crucial role in today's control technology, such as in smart bikes, cars, and drones. They need to perform tasks promptly with limited processing power. However, the complexity of these tasks can lead to longer processing times, increasing the risk of failing to compute control signals within the required time frame. This could potentially damage the system and endanger users. This talk summarizes several ye ars of research focused on analyzing embedded controllers, highlighting the effect of computational delays that lead to missed deadlines. We will discuss different types of timing violations, including bursts of missed deadlines, constrained patterns, and sporadic misses. By integrating these scenarios with detailed insights into the control systems' implementation and correlating our analysis with practical applications, we aim to provide a comprehensive view of deadline-miss tolerance for embedded controllers. Finally, we will demonstrate the validity of our findings through extensive simulations and real-world experiments, showcasing the practical implications of our research.

Keywords: Identification for hybrid systems, Switched systems, Optimization algorithms
Abstract: We propose an online identification scheme for discrete-time piece-wise affine state-space models based on a system of adaptive algorithms running in two timescales. A stochastic approximation algorithm implements an online deterministic annealing scheme at a slow timescale, estimating the partition of the augmented state-input space that defines the switching signal. At the same time, an adaptive identification algorithm, running at a higher timescale, updates the parameters of the local models based on the estimate of the switching signal. Identifiability conditions for the switched system are discussed and convergence results are given based on the theory of two-timescale stochastic approximation. In contrast to standard identification algorithms for piece-wise affine systems, the proposed approach progressively estimates the number of modes needed and is appropriate for online system identification using sequential data acquisition. This progressive nature of the algorithm improves computational efficiency and provides real-time control over the performance-complexity trade-off, desired in practical applications. Experimental results validate the efficacy of the proposed methodology.

Keywords: Identification, Nonlinear system identification, Identification for control
Abstract: This paper introduces a novel direct approach to system identification of dynamic networks with missing data based on maximum likelihood estimation. Dynamic networks generally present a singular probability density function, which poses a challenge in the estimation of their parameters. By leveraging knowledge about the network's interconnections, we show that it is possible to transform the problem into a more tractable form by applying linear transformations. This results in a nonsingular probability density function, enabling the application of maximum likelihood estimation techniques. Our preliminary numerical results suggest that when combined with global optimization algorithms or a suitable initialization strategy, we are able to obtain a good estimate of the dynamics of the internal systems.

Keywords: Identification, Signal processing, Stochastic systems
Abstract: Prediction error (PE) and maximum likelihood (ML) methods are often treated as synonyms when identifying linear dynamic systems from Gaussian data. It is shown how these methods differ when specifically dealing with errors-in-variables problems. These problems can modeled using multivariable times series with a specific internal structure. In such situations the ML estimates have lower variances than the PE estimates. Explicit expressions for the covariance matrices of the estimates are given and analyzed. For the special case when the unperturbed input is white noise it is shown that the PE estimate is not identifiable, while the ML estimates still have quite small variances. In such situations ML is thus much superior to the PE estimates. Another special case concerns non-Gaussian data. In that case a pseudo-ML estimate (using the ML criterion as if the data were Gaussian) will no longer be superior to the PE estimate in terms of error variances.

Keywords: Identification for hybrid systems, Nonlinear system identification, Optimization algorithms
Abstract: The increased frequency and severity of extreme weather events are challenging traditional static control strategies for stormwater detention ponds, which are critical components in urban water management infrastructures. This paper introduces a compositional control methodology, rooted in formal verification and reinforcement learning, and tailored for the synthesis of a joint optimal control strategy for the management of distributed but interconnected ponds. Combining hybrid Markov Decision Processes (HMDPs) and reinforcement learning via Uppaal Stratego, the compositional control strategy provides a balance between fully centralized and decentralized control strategies, both in terms of quality and computational complexity. Based on a real-world case study we analyze and compare the proposed methodology and show how the synthesized strategies can control the timing and volume of water discharge, reducing the risk of overflow caused by the simultaneous discharge of rainwater collected in multiple ponds.

Keywords: Identification for hybrid systems, Switched systems, Identification for control
Abstract: This paper serves as a first identification step in a two-step model-based control synthesis problem of switched linear systems (SLSs). More precisely, we present an algorithm that addresses the realization of the multi-input/multi-output MIMO-SLSs from Markov parameters under mild assumptions on the dwell-times and the submodels. A key point of the proposed approach is the introduction of the forward and backward correction operators, which relieves the dependence on the choice of basis vectors in computing state-space matrices of the realizations. A numerical example illustrates the derived results.

Keywords: Identification, Identification for control
Abstract: This paper introduces a dual input-output parameterization (dual IOP) for the identification of linear time-invariant systems from closed-loop data. It draws inspiration from the recent input-output parameterization developed to synthesize a stabilizing controller. The controller is parameterized in terms of closed-loop transfer functions, from the external disturbances to the input and output of the system, constrained to lie in a given subspace. Analogously, the dual IOP method parameterizes the unknown plant with analogous closed-loop transfer functions, also referred to as dual parameters. In this case, these closed-loop transfer functions are constrained to lie in an affine subspace guaranteeing that the identified plant is stabilized by the known controller. Compared with existing closed-loop identification techniques guaranteeing closed-loop stability, such as the dual Youla parameterization, the dual IOP neither requires a doubly-coprime factorization of the controller nor a nominal plant that is stabilized by the controller. The dual IOP does not depend on the order and the state-space realization of the controller either, as in the dual system-level parameterization. Simulation shows that the dual IOP outperforms the existing benchmark methods.

Keywords: Optimal control, Predictive control for nonlinear systems, Autonomous robots
Abstract: Direct shooting is an efficient method to solve numerical optimal control. It utilizes the Runge-Kutta scheme to discretize a continuous-time optimal control problem making the problem solvable by nonlinear programming solvers. However, conventional direct shooting raises a contradictory dynamics issue when using an augmented state to handle {high-order} systems. This paper fills the research gap by considering the direct shooting method for {high-order} systems. We derive the modified Euler and Runge-Kutta-4 method to transcribe the system dynamics constraint directly. Additionally, we prove that our proposed methods are convergent. A set of benchmark numerical optimal control problems shows that our methods provide more accurate solutions than existing approaches.

Keywords: Predictive control for linear systems, Intelligent systems, Automotive
Abstract: In this paper we present a novel approach to address the lane change maneuver for Autonomous Vehicles (AVs). We frame this challenge as a parametric Model Predictive Control (MPC) problem, recognizing that the successful execution of lane changes requires two critical components: the decision on when to initiate these maneuvers and the actual generation of the maneuvers. Unlike existing approaches that often decouple decision-making and planning tasks, leading to performance bottlenecks and conservative solutions, our approach adopts a hybrid perspective. Specifically, we tackle the problem of capturing the lane change decision-making through an upper-level policy search, which, in turn, guides an MPC policy in generating the maneuver. The proposed approach leverages a weighted maximum likelihood technique for policy learning, effectively optimizing the lane change strategy. Furthermore, we incorporate self-supervised learning techniques to adapt to dynamic and online scenarios, ensuring that the AV can handle unexpected changes in its environment. We provide numerical results demonstrating the effectiveness of the proposed approach, highlighting its potential for improving AV maneuvering in dynamic environments.

Keywords: Predictive control for linear systems, Model/Controller reduction, Constrained control
Abstract: We address the challenge of dimension reduction in the discrete-time optimal control problem which is solved repeatedly online within the framework of model predictive control. Our study demonstrates that a reduced-order approach, aimed at identifying a suboptimal solution within a low-dimensional subspace, retains the stability and recursive feasibility characteristics of the original problem. We present a necessary and sufficient condition for ensuring initial feasibility, which is seamlessly integrated into the subspace design process. Additionally, we employ techniques from optimization on Riemannian manifolds to develop a subspace that efficiently represents a collection of pre-specified high-dimensional data points, all while adhering to the initial admissibility constraint.

Keywords: Predictive control for linear systems, Optimization, Optimal control
Abstract: Algorithms for solving Quadratic Programs (QPs) are indispensable in the research of Model Predictive Control (MPC) of linear dynamical systems. In a recent paper, Raghunathan [1] proposed a novel Homogeneous Quadratic Program (HQP) formulation that can determine optimality and infeasibility of QPs under assumptions that are readily satisfied for MPC. In this paper, we develop a structure exploiting factorization for the linear systems that occur when solving the QPs arising in MPC using the HQP formulation. We have developed a C++ framework (QOACH-MPC) that abstracts the structure exploiting factorization from the algorithm implementation, and makes it convenient for implementing and testing algorithms for MPC. Currently, QOACH-MPC implements an Interior Point Method (IPM) and Semismooth Newton Method (SNM) for solving the HQP, where the step computation exploits the structure in MPC. We demonstrate how our framework can be leveraged to produce a mixed algorithmic strategy for solving the closed-loop MPC problem.

Keywords: Predictive control for nonlinear systems, Robotics, Neural networks
Abstract: Model Predictive Path Following is an attractive solution for motion control of mobile robots and other autonomous systems. It requires solving a non-convex constrained optimization problem at each sampling period. We focus on a general approach to approximate Model predictive Path-Following Control (MPFC) for a mobile robot using a Deep neural network (DNN) that learns a set of base MPFC representations via path primitives. We show that using simple algebraic operations, any path can be followed with reasonable accuracy, and we illustrate how to apply this approach for paths described by linear segments and parabolic blends that can be generated by a robotic path planning algorithm. Compared to the computational requirements of MPFC, our proposed approach requires significantly less memory and the execution speed is two orders of magnitudes faster. This makes our approach suitable for microcontroller implementation, with only a small degradation of the path-following accuracy compared to online MPFC.

Keywords: Predictive control for nonlinear systems, Robotics, Optimal control
Abstract: Redundant robotic systems allow for the simultaneous execution of multiple tasks. It is often desirable to have priority between these tasks, such that lower priority tasks do not interfere with higher priority tasks. Many existing methods for task-priority control, while providing stability and strict priority between tasks, do not take into account the overall performance of the closed-loop system. This paper presents an optimization-based framework for dynamic task-priority control of redundant robotic systems, providing strict priority between tasks while improving the performance of the closed-loop system relative to a user-defined performance metric. The method is based on using a nominal dynamic task-priority control law together with a hierarchical control Lyapunov function based model-predictive control method. The proposed method is validated in simulation on a redundant robotic system, where it is shown to provide improved performance over existing dynamic task-priority control methods.

Keywords: Optimization, Optimal control of communication networks, Distributed estimation over sensor nets
Abstract: We propose a novel multi-agent approach for auto-adjusting OFDM parameters for underwater acoustic communication that utilizes distributed optimization to perform a collaborative choice. The algorithm enhances overall communication performance among all agents, and makes its decision based on environmental information that is first actively collected from each agent at the beginning of their mission, and then communicated via opportune statistics of such sampled information. The proposed method does not rely on link feedback from receivers; while based on distributed optimization (and thus requiring data transmission among the agents), the approach does not introduce any overhead during data transmission and can be used as a separate process at any preferred moment prior to the data transmission. We present numerical comparisons based on simulation results to demonstrate the dependence of the effectiveness of the proposed approach with respect to different marine conditions that may be encountered in field missions, and the dependence of its efficiency on which optimization algorithm is chosen. The overall results indicate that for a various set of conditions the approach may lead to a more effective usage of the underwater acoustic channel.

Keywords: Optimization, Optimization algorithms, Decentralized control
Abstract: Online feedback optimization is a controller design paradigm for optimizing the steady-state behavior of a dynamical system. It employs an optimization algorithm as a feedback controller and utilizes real-time measurements to bypass knowing exact plant dynamics and disturbances. Different from existing centralized settings, we present a fully decentralized feedback optimization controller for networked systems to lift the communication burden and improve scalability. We approximate the overall input-output sensitivity matrix through its diagonal elements, which capture local model information. For the closed-loop behavior, we characterize the stability and bound the sub-optimality due to decentralization. We prove that the proposed decentralized controller yields solutions that correspond to the Nash equilibria of a non-cooperative game. Simulations for a voltage control problem on a direct current power grid corroborate the theoretical results.

Keywords: Optimization, Optimization algorithms
Abstract: The application of a zeroth-order scheme for minimising Polyak-Lojasewicz (PL) functions is considered. The framework is based on exploiting a random oracle to estimate the function gradient. The convergence of the algorithm to a global minimum in both constrained and unconstrained cases along with their corresponding complexity bounds are presented. The theoretical results are demonstrated via numerical examples.

Keywords: Optimization, Stochastic control, Safety critical systems
Abstract: Signal Temporal Logic (STL) offers an expressive formalism for describing complex high-level tasks in dynamical systems. This paper introduces a time-varying Control Barrier Function (CBF) for control-affine nonlinear stochastic systems to fulfill STL specifications. The CBFs are used in a robust optimization problem to provide a lower bound on the satisfaction probability of a given STL specification with a predetermined robustness level. We present an online control synthesis approach to minimize a performance function while having the required satisfaction guarantee. We finally provide a tractable solution to the robust optimization for STL with linear and quadratic predicate functions. To illustrate the effectiveness of the method, we apply it to a simple linear case study and to the path-planning problem for a nonlinear wheeled mobile robot.

Keywords: Optimization, Process control, Identification for control
Abstract: This paper introduces a new approach aimed at expediting the auto-tuning of PI controllers during the commissioning phase. This approach is based on the exploitation of the process model knowledge and Bayesian optimization capabilities. The process unfolds in a sequence of steps: initially, the quality of the model is improved through the identification of unknown or uncertain parameters of the model. Subsequently, this refined model is used for searching the optimal configuration of PI controller. The outcomes obtained, encompassing the initial estimate, upper and lower bounds, and the Gaussian process mean, are then harnessed to initiate the Bayesian optimization process in the commissioning phase. By initializing adequately the Bayesian optimization, a significant reduction in the number of iterations required to reach the optimizer's optimal solution can be achieved. The approach efficiency is demonstrated through its application to a thermal plant.

Keywords: Optimal control, Markov processes, Variational methods
Abstract: The objective of this research is to identify optimal control formulations or similar problems, that can be solved by practising inference on probabilistic graph models instead of solving temporal nonlinear optimization problems. This is an active research topic in the Reinforcement Learning and control community that is better known as Control as Inference. Inference on probabilistic graph models is a computational process that is easily automated for example using message passing. In this contribution we show that Partially Observable Markov Decision Problems with multiplicative reward structure can be represented by an equivalent Maximum Likelihood Estimation problem. Subsequently the estimation problem can be treated by means of the Expectation-Maximization algorithm. We show that maximization of the Evidence Lower Bound can be reinterpreted as a probabilistic control problem which is itself a density matching interpretation of Control as Inference. The associated probabilistic policy can be represented as a conditional density and can be calculated by message passing on the probabilistic graph model. These results provide a unified account of probabilistic control and control as inference with multiplicative reward structures under partial observability.

Keywords: Autonomous robots, Nonlinear system theory, Robust control
Abstract: This paper proposes a concept of feedback concavification to the control of an underactuated autonomous underwater vehicle (AUV) with 6-DOF, subject to actuator saturation, to improve transient performance and robustness, taking into account the presence of unknown model dynamics and disturbances. To address the underactuated problem under the dissipation framework, Euclidean geometry is utilized to match the dimensions of the input and output. Therefore, an interconnected architecture for the AUV system is proposed, enabling the AUV system to be transformed into interconnected passive systems via feedback within this architecture with guaranteed asymptotic stability. The concave passivity is then applied to the interconnected passive systems to handle uncertainties, disturbances, and actuator saturation problems. The proposed method with assigned concavity is effective in different scenarios with fast transient response and the decreasing L2-gain under actuator saturation. Numerical simulations have demonstrated the effectiveness of the proposed interconnected passive architecture and the feedback concavification approach in improving the control performance of the underactuated AUV.

Keywords: Autonomous robots, Optimal control, Robotics
Abstract: This paper presents an analytical formulation for the time-optimal trajectory planning problem for unicycle robots in structured environments. We describe the environment as a sequence of rectangular corridors and compute the trajectory as a sequence of time-optimal motion primitives defined according to Pontryagin's minimum principle. The proposed approach uses a heuristic to place the center of arc maneuvers and, in certain situations, uses a turn on-the-spot primitive as initial or final maneuver to achieve time-optimality and obstacle avoidance. The computation of the trajectory within each corridor is decoupled and, therefore, the results can be applied to an arbitrarily long sequence of corridors while allowing parallel computations. The effectiveness of the approach is demonstrated by means of an example and Monte Carlo simulation: the suboptimality of the analytic motion is less than 1%, while the solution time is two orders of magnitude less than that of an optimal control problem formulation.

Keywords: Autonomous robots, Robotics, Predictive control for linear systems
Abstract: The current state-of-the art motion planners for mobile robots typically do not consider the future movement of moving obstacles. Instead they work by rapid replanning, which makes them reactively adapt to any changes in the environment. This can result in a sub-optimal behavior, which we address in this work by proposing a predictive motion planner that integrates motion predictions into all planning steps. We demonstrate the validity of our approach by evaluating our proposed planner in a dynamic environment where the robot moves slower than the moving obstacles. We benchmark our predictive planner with a reactive planning approach and observe better performance, both in avoiding collisions and maintaining the robots position in the goal region.

Keywords: Autonomous robots, Robotics
Abstract: As the interest in mobile robots navigation on public sidewalks increases, so does the attention given to local planners capable of navigating around humans in a socially acceptable way. This paper presents a socially aware version of the Dynamic Window Approach planner. The DWA is augmented with an additional cost function, which predicts pedestrian trajectories using the Social Forces Model. Scoring of the robot control action is achieved by weighting the disturbance the robot causes to pedestrians. The approach is validated in a simulation environment with realistic pedestrian motions, showing superior performance with respect to the original DWA as well as to a distance-based scoring method.

Keywords: Autonomous robots, Robotics, Automotive
Abstract: Despite the number of works published in recent years, vehicle localization remains an open, challenging problem. While map-based localization and SLAM algorithms are getting better and better, they remain a single point of failure in typical localization pipelines. This paper proposes a modular localization architecture that fuses sensor measurements with the outputs of off-the-shelf localization algorithms. The fusion filter estimates model uncertainties to improve odometry in case absolute pose measurements are lost entirely. The architecture is validated experimentally on a real robot navigating autonomously proving a reduction of the position error of more than 90% with respect to the odometrical estimate without uncertainty estimation in a two-minute navigation period without position measurements.

Keywords: Autonomous robots, Agents and autonomous systems, Optimization algorithms
Abstract: In this paper, we propose an alternating optimization method to address a time-optimal trajectory generation problem. Different from the existing solutions, our approach introduces a new formulation that minimizes the overall trajectory running time while maintaining the polynomial smoothness constraints and incorporating hard limits on motion derivatives to ensure feasibility. To address this problem, an alternating peak-optimization method is developed, which splits the optimization process into two sub-optimizations: the first sub-optimization optimizes polynomial coefficients for smoothness, and the second sub-optimization adjusts the time allocated to each trajectory segment. These are alternated until a feasible minimum-time solution is found. We offer a comprehensive set of simulations and experiments to showcase the superior performance of our approach in comparison to existing methods.

Keywords: Control over communication, Switched systems
Abstract: This paper considers control systems with failures in the feedback channel, that occasionally lead to loss of the control input signal. A useful approach for modeling such failures is to consider window-based constraints on possible loss sequences, for example that at least r control attempts in every window of s are successful. A powerful framework to model such constraints are weakly-hard real-time constraints. Various approaches for stability analysis and the synthesis of stabilizing controllers for such systems have been presented in the past. However, existing results are mostly limited to asymptotic stability and do not consider performance measures such as the resulting ℓ2-gain. To address this problem, we adapt a switched system description where the switching sequence is constrained by a graph that captures the loss information. We present an approach for ℓ2-performance analysis involving linear matrix inequalities (LMI). Further, leveraging a system lifting method, we propose an LMI-based approach for synthesizing state- feedback controllers with guaranteed ℓ2-performance. The results are illustrated by a numerical example.

Keywords: Optimal control, Switched systems, Automotive
Abstract: Trajectory planning in automated driving typically focuses on satisfying safety and comfort requirements within the vehicle’s onboard sensor range. This paper introduces a method that leverages anticipatory road data, such as speed limits, road slopes, and traffic lights, beyond the local perception range to optimize energy-efficient braking trajectories. For that, coasting, which reduces energy consumption, and active braking are combined to transition from the current vehicle velocity to a lower target velocity at a given distance ahead. Finding the switching instants between the coasting phases and the continuous control for the braking phase is addressed as an optimal trade-off between maximizing coasting periods and minimizing braking effort. The resulting switched optimal control problem is solved by deriving necessary optimality conditions. To facilitate the incorporation of additional feasibility constraints for multi-phase trajectories, a suboptimal alternative solution based on parametric optimization is proposed. Both methods are compared in simulation.

Keywords: Switched systems, Stability of nonlinear systems
Abstract: This paper studies stochastic input-to-state stabil- ity of impulsive evolution equations with randomly distributed jump instants. We model these random jump instants via a Poisson process. Our approach derives the stability conditions by employing candidate Lyapunov functions parameterized by nonlinear rates. We apply our results to the cooling mechanism of a metal string modeled by a partial differential equation with jumps.

Keywords: Switched systems
Abstract: We consider the analytical controller design for a class of switched linear systems. Under suitable system assumptions, we propose a method using eigenstructure assignment that guarantees the closed-loop switched system is globally asymptotically stable under arbitrary switching signals, and the outputs achieve monotonic step reference tracking from all initial conditions. Additionally, the output signals from both subsystems can be made identical, so the effects of the system switching are not noticeable from the output. A constructive algorithm is provided that yields suitable feedback matrices, and the method is illustrated with a numerical example.

Keywords: System reconfiguration, Switched systems, Process control
Abstract: Control loops trend towards cyber-physical systems and come with corresponding challenges that complex IT systems from other domains share, such as increased effort to integrate components into a system. Modern service-oriented architectures can help address these challenges by decoupling component development from system integration. This paper presents a concept to model control loop elements as services that are flexibly integrated at runtime using a central entity called orchestrator. Our concept is suitable for a general control system, is capable of running on embedded and general-purpose computers, and can ensure deterministic computations. We apply our approach to an experimental setup of a three-tank system and implement multiple control loops using a set of services and an orchestrator. We show that using our architecture, the service composition is easy to change dynamically at runtime and thus realize a retrofit of the process control.

Keywords: Switched systems, Linear systems, Fault tolerant systems
Abstract: In this paper, we introduce the difference structure to pre- and post-switch systems, to propose a new switching L2 gain for evaluating the fluctuations around an output of a specified transfer property after a system switch. Moreover, we apply it to the initial state design of a newly-activated controller designed by model matching to establish its potential practicality as a design index.

Keywords: Computational methods, Electrical machine control, Optimization
Abstract: For the application of modern control and optimization techniques to power electronics applications, performing intensive computations on a microsecond-scale is of paramount importance. The optimization algorithms involved are in most of the cases NP-hard, involving integer variables and non-linear objectives; their real-time implementation is done on embedded control platforms in the form of field-programmable gate arrays (FPGAs). These enable fast computation but also feature limited hardware resources that constrain the size of the problems that can be tackled. In this paper, we present a scalable digital hardware (HW) architecture implemented on an FPGA for the real-time implementation of a branch-and-bound algorithm for mixed-integer optimization. The need for such a solver stems from the concept of converter modulation, where an optimal voltage path of a certain length has to be reconstructed by the appropriate choice of a combination of integer variables. We showcase how the geometrical properties of the optimization problem can be exploited to enable a solution in microseconds, and how the FPGA design can be modularized to ensure that the HW resources are adequate even when considering longer voltage reconstruction paths which translate to an exponential increase in problem complexity.

Keywords: Electrical power systems, Energy systems, Distributed control
Abstract: In this paper, we adopt an energy perspective to analyze the stability of converter-dominated dc grids under a hierarchical (primary/secondary) optimal control strategy based on distributed communications, and its robustness against cyber-threats. First, we begin by showing that both the decentralized droop-controlled dc microgrid, as well as the distributed secondary controller, admit a port-Hamiltonian description. Second, we exploit the fact that the closed-loop system can be interpreted as a lossy-interconection between their (incremental) models to prove stability for the unperturbed system. Third, we analyze the effect of malicious attacks on the (linear) system and robustify the control system such that resilience against cyber threats always is ensured at steady state. Additionally, we show that by adequately tuning the controllers we are able to significantly reduce the attack influence on the desired steady state. Finally, we use time-domain simulations to support our findings in a case study involving a low-voltage DC microgrid.

Keywords: Electrical machine control, Nonlinear system theory, Constrained control
Abstract: In this paper, a novel nonlinear controller for induction motors used in EV applications is proposed to achieve feld-oriented operation with minimum losses at the steady state, while inherently guaranteeing the required operating constraints. By taking the expression of the total (iron and copper) losses of the motor, an optimization problem is formulated to minimize the motor losses under the required equality and inequality constraints (torque, voltage and current constraints). Opposed to conventional loss-minimizing control that requires the solution of the entire optimization problem and continuous update of the reference motor currents in order to guarantee all necessary constraints, in this paper, a novel bounded controller is introduced with a specifc nonlinear dynamic structure that inherently accomplishes all voltage and current constraints, while achieving feld-oriented operation. Hence, a simplifed optimization problem is required to be solved once offline to obtain the desired property that the motor currents should satisfy in order to minimize the entire motor losses, thus simplifying the controller implementation. In order to verify the theoretical contribution, the proposed controller is directly compared to the well-known feld-oriented control and the conventional loss-minimizing control for the same induction motor control scenario.

Keywords: Electrical power systems, LMI's/BMI's/SOS's, Network analysis and control
Abstract: This paper is concerned with the feasibility of the power flow in DC power power grids with constant power loads. We introduce the notion of distance to infeasibility as a voltage stability index and robustness measure for power flow feasibility. In particular, we study the p-norm distance to infeasibility in the domain of the constant power loads, and show how this distance may be expressed as a mathematical program. Necessary and sufficient matrix inequalities are presented that guarantee a minimal p-norm distance between a given vector of power demands and the boundary of infeasibility. For the cases 1-norm and infinity-norm distance we show that the condition can be formulated as (multiple) linear matrix inequalities, whereas in all other cases the matrix inequalities are strictly concave and thus non-convex. For the 2-norm distance we show that the distance to infeasibility may be computed via bilinear matrix inequalities. A numerical example for the infinity-norm distance to infeasibility for the 39 bus New England power grid is provided.

Keywords: Optimization algorithms, Predictive control for linear systems, Power electronics
Abstract: In the last years, model predictive control (MPC) has become a significant competitor to conventional control strategies for power electronic applications. In particular, direct MPC using sphere decoding algorithms (SDAs) as optimizers have gained popularity in this context. Besides such specialized optimization algorithms, it is possible to improve the MPC performance by dimensional reduction of the optimization problem using Laguerre polynomials (LaPs). LaP based MPC has already been proven advantageous for example in robotic and marine applications. This paper presents an approach to link the SDA and LaPs by formulating a Laguerre polynomial based sphere decoding algorithm (LaP-SDA) for the control of inverters on the modulation-level. Following some introductions to the SDA and LaPs, the optimization problem and its particular structure for the SDA is transformed using sets of LaPs. This modification of the SDA results in a new admissible set and an additional optimization problem to find the optimal configuration of the LaPs. Moreover, it changes the shape of the search tree tailoring it wider but more shallow. Finally, the LaP-SDA is verified in a simulation using an example system. There, it is shown that, with a suitable configuration, the results of the SDA and LaP-SDA are identical. However, the simulation does not indicate a performance benefit of the LaP-SDA mainly because of the size of the admissible set. Nevertheless, prospective modifications to improve its performance and further applications of the LaP-SDA are presented.

Keywords: Predictive control for linear systems, Optimal control, Power electronics
Abstract: Although classical model predictive control with finite control sets (FCS-MPC) is quite a popular control method, particularly in the realm of power electronics systems, its direct data-driven predictive control (FCS-DPC) counterpart has received relatively limited attention. In this paper, we introduce a novel reformulation of a commonly used DPC scheme that allows for the application of a modified sphere decoding algorithm, known for its efficiency and prominence in FCS-MPC applications. We test the reformulation on a popular electrical drive example and compare the computation times of sphere decoding FCS-DPC with an enumeration-based and a MIQP method.

Keywords: Traffic control, Predictive control for nonlinear systems, Safety critical systems
Abstract: This paper addresses the problem of traffic prediction and control of autonomous vehicles on highways. A modified Interacting Multiple Model Kalman filter algorithm is applied to predict the motion behavior of the traffic participants by considering their interactions. A scenario generation component is used to produce plausible scenarios of the vehicles based on the predicted information. A novel integrated decision making and control system is proposed by applying a Scenario-based Model Predictive Control approach. The designed controller considers safety, driving comfort, and traffic rules. The recursive feasibility of the controller is guaranteed under the inclusion of the 'worst case' as an additional scenario to obtain safe inputs. Finally, the proposed scheme is evaluated using the HighD dataset. Simulation results indicate that the vehicle performs safe maneuvers in different traffic situations under the designed control framework.

Keywords: Traffic control, Transportation systems, Optimization
Abstract: This paper considers the stability and optimality properties of traffic demand management schemes, motivated by the integration of smart monitoring and control schemes in traffic networks. First, a suitable optimization problem is formulated that aims to obtain demand input values that maximize the throughput within traffic networks. We show that optimal solutions to this problem may lead to unstable behaviour, revealing a trade-off between stability and optimality. To address this issue, we analytically study the stability properties of traffic networks at the presence of constant demand input and provide suitable local conditions that guarantee stability when the system's equilibrium densities are strictly within the free-flow region, but not at the critical density. The latter case is significant, since the maximum throughput behaviour coincides in many cases with the local critical density. We resolve this by proposing a decentralized proportional demand control scheme and suitable local design conditions such that stability is guaranteed. Our analytic results are validated with numerical simulations in a 3-region system that demonstrate the effectiveness and practicality of the presented results.

Keywords: Traffic control, Sampled data control, Transportation systems
Abstract: This paper explores sampled-data techniques to achieve asymptotic string stability in a platoon of autonomous vehicles. This is done making use of both microscopic and macroscopic data that are, however, often available at distinct time instants. The proposed mesoscopic controller is demonstrated to operate effectively, regardless of the involved sampling periods. The theoretical findings are validated through simulations.

Keywords: Traffic control, Transportation systems, Large-scale systems
Abstract: In this paper, we advance the state-of-the-art of Max-Pressure traffic signal control by considering modeling aspect of enhancing the calculation of pressure function.

First, we stress out that the conventional pressure model does not consider the distribution of the vehicles between the lanes, and it over-calculates the pressure function when multiplying with the saturation flow of the movement. We conducted a thorough analysis of the existing pressure calculation model. The model's inability to distribute pressure equitably could skew the controller's policy optimization, potentially leading to unfair decisions.

Second, the impact of introduced modification is investigated through simulation case studies. The results indicate that the Max-Pressure control policy has been significantly improved. This underlines the importance of accurately characterizing the parameters within the Max-Pressure controller, which is crucial for improved outcomes and more effective decision-making processes.

Keywords: Traffic control, Transportation systems, Intelligent systems
Abstract: Recent years introduced the wealth of available vehicle location data from connected vehicles, cell phones and navigation systems alike. This data can be used to improve the existing transportation network in various ways. Among the most promising approaches is traffic light optimization. Traffic light optimization has the potential to reduce traffic congestion, air pollution and GHG emissions. The first step in such optimization is the understanding of the existing traffic light plans. Such plans are periodic but in practice often start every day at arbitrary times, making it hard to align traffic trajectories from various days towards the analysis of the plan. We provide an estimation model for estimating the daily start time of periodic plans of traffic lights. The study is inspired by real-world data provided for instance by navigation applications. We analyze the accuracy of such computations as a function of the characteristics of the sampled traffic and the length of the evaluated time period.

Keywords: Traffic control, Predictive control for linear systems, Robotics
Abstract: This paper presents an innovative approach for optimizing the clearance of emergency vehicles at traffic intersections by employing Model Predictive Control (MPC) in conjunction with reference trajectory generation. The algorithm operates in two distinct phases: offline static map generation and online dynamic trajectory planning. In the offline phase, the algorithm constructs a static map of the intersection, approximating the drivable area with a polytope covering. In the online phase, the algorithm continuously gathers real-time data on the positions of all vehicles present at the intersection. Based on mixed-integer programming techniques, our algorithm dynamically generates reference trajectories for each vehicle, including the emergency vehicle to facilitate the fastest possible passage for the emergency vehicle to its target location while ensuring the safe clearance of the path ahead. We demonstrate the feasibility and effectiveness of our model predictive control-based algorithm in enhancing the response time of emergency vehicles and minimizing intersection congestion, ultimately contributing to the improvement of urban safety and emergency response services.

Keywords: Agents and autonomous systems, Modeling, Chaotic systems
Abstract: We consider a set of consumers in a city or town whose opinion is governed by a continuous opinion and discrete action (CODA) dynamics model. This dynamics is coupled with an observation signal dynamics, which defines the information on the common pollution that the consumers can access. We show that the external observation signal has a significant impact on the asymptotic behavior of the CODA model. When the coupling is strong, it induces either a chaotic behavior or convergence towards a limit cycle. When the coupling is weak, a more classical behavior characterized by local agreements in polarized clusters is observed. In both cases, conditions under which clusters of consumers don't change their actions are provided. Numerical examples are provided to illustrate the derived analytical results.

Keywords: Network analysis and control, Complex systems, Biological systems
Abstract: The paper deals with the analysis of a discrete-time networked competitive bivirus susceptible-infected-susceptible (SIS) model. More specifically, we suppose that virus~1 and virus~2 are circulating in the population and are in competition with each other. We show that the model is strongly monotone, and that, under certain assumptions, it does not admit any periodic orbit. We identify a sufficient condition for exponential convergence to the disease-free equilibrium (DFE). Assuming only virus 1 (resp. virus 2) is alive, we establish a condition for global asymptotic convergence to the single-virus endemic equilibrium of virus 1 (resp. virus 2) - our proof does not rely on the construction of a Lyapunov function. Assuming both virus 1 and virus 2 are alive, we establish a condition which ensures local exponential convergence to the single-virus equilibrium of virus 1 (resp. virus 2). Finally, we provide a sufficient (resp. necessary) condition for the existence of a coexistence equilibrium.

Keywords: Network analysis and control, Control over networks, Large-scale systems
Abstract: In this paper, we examine how adding memory to nodes of a network system impacts its controllability properties. Specifically, we analyze the behavior of the control energy of a continuous-time linear network system and that of its lifted version, obtained by allowing each node to have nontrivial internal dynamics. We discuss how to compare the effect of a control input on the original and lifted network, and show that, for line networks, adding memory may reduce the worst-case control energy by a factor that is exponential in the network size.

Keywords: Network analysis and control, Distributed cooperative control over networks, Robotics
Abstract: The safe control of multi-robot swarms is a challenging and active field of research, where common goals include maintaining group cohesion while simultaneously avoiding obstacles and inter-agent collision. Building off our previously developed theory for distributed collaborative safety-critical control for networked dynamic systems, we propose a distributed algorithm for the formation control of robot swarms given individual agent dynamics, induced formation dynamics, and local neighborhood position and velocity information within a defined sensing radius for each agent. Individual safety guarantees for each agent are obtained using rounds of communication between neighbors to restrict unsafe control actions among cooperating agents through safety conditions derived from high-order control barrier functions. We provide conditions under which a swarm is guaranteed to achieve collective safety with respect to multiple obstacles using a modified collaborative safety algorithm. We demonstrate the performance of our distributed algorithm via simulation in a simplified physics-based environment.

Keywords: Agents networks, Concensus control and estimation
Abstract: Modeling the collective response to an emergency is a problem of paramount importance in social science and risk management. Here, we leverage the social-psychology literature to develop a mathematical model tailored to such a problem. In our model, a network of individuals revises their risk perception by processing information broadcast by the institution and shared by peers, accounting for heterogeneity in terms of individuals' trust in institutions, peers, and risk sensitivity. Analyzing the model, we establish that the temporal average opinions of the individuals converge to a steady state and, under some assumptions, we are able to analytically characterize such a steady state, shedding light on how the individuals' heterogeneous risk perception shapes the collective response.

Keywords: Behavioural systems, Agents networks, Network analysis and control
Abstract: This paper investigates the problem of maximizing social power for a group of agents, who participate in multiple meetings described by independent Friedkin-Johnsen models. A strategic game is obtained, in which the action of each agent (or player) is her stubbornness over all the meetings, and the payoff is her social power on average. It is proved that, for all but some strategy profiles on the boundary of the feasible action set, each agent’s best response is the solution of a convex optimization problem. Furthermore, even with the non-convexity on boundary profiles, if the underlying networks are given by a fixed complete graph, the game has a unique Nash equilibrium. For this case, the best response of each agent is analytically characterized, and is achieved in finite time by a proposed algorithm.

Keywords: Machine learning, Reduced order modeling, Automotive
Abstract: For many industrial processes, a digital twin is available, which is essentially a highly complex model whose parameters may not be properly tuned for the specific process. By relying on the availability of such a digital twin, this paper introduces a novel approach to data-driven control, where the digital twin is used to generate samples and suitable controllers for various perturbed versions of its parameters. A supervised learning algorithm is then employed to estimate a direct mapping from the data to the best controller to use. This map consists of a model reduction step, followed by a neural network architecture whose output provides the parameters of the controller. The data-to-controller map is pre-computed based on artificially generated data, but its execution once deployed is computationally very efficient, thus providing a simple and inexpensive way to tune and re-calibrate controllers directly from data. The benefits of this novel approach are illustrated via numerical simulations.

Keywords: Reduced order modeling, Identification
Abstract: We study the model reduction by moment matching problem for linear systems in a data-driven framework. We show that reduced-order models can be directly computed from data without knowledge of the structure of the signal generator or of its internal state. The reduced-order models thus obtained match the moments of the unknown underlying system asymptotically. Our construction provides a simple way to enforce additional constraints in the reduced-order model. We demonstrate the applicability of the results using data from a high-dimensional model of a building.

Keywords: Reduced order modeling, Model/Controller reduction, Modeling
Abstract: The continuously increasing amount of noisy data demands the development of accurate and efficient models for analysis, modeling, and control. In this article, we propose a novel data-driven moment matching method which employs Tikhonov regularization in the Reproducing Kernel Hilbert Spaces (RKHSs). Specifically, considering a realistic scenario in which the system's plant is unknown and only noisy measured data are available, we provide an estimation of the moment of the unknown plant by solving a regularized optimization problem on RKHS. For, we first demonstrate that the estimation of the moment can be improved via tuning the regularization term, and further, we show under which condition the effect of the transient improves the performance of the estimation. Then, we construct a parameterized model characterized by a kernel-based output mapping. Finally, the proposed data-driven approach is validated and discussed by means of a DC-to-DC Ćuk converter driven by a Van der Pol oscillator.

Keywords: Identification, Linear systems, Model/Controller reduction
Abstract: We consider the problem of finding the best least squares realization of an autonomous single-output linear time-invariant dynamical system, given a sequence of non-model-compliant output data. We characterize the solution set of the identification problem and derive novel properties of the optimal models. We show how the global minima of the problem follow from the eigentuples of a multiparameter eigenvalue problem and illustrate this result using several numerical `toy examples' in which we compute the globally optimal solution(s) explicitly.

Keywords: Linear systems, Identification, Computational methods
Abstract: The reduced-order modeling of a system from data (also known as system identification) is a classical task in system and control theory and well understood for standard linear systems with the so-called Loewner framework as one of many established approaches. In the case of descriptor systems for which the transfer function is not proper anymore, recent research efforts have addressed strategies to deal with the non-proper parts more or less explicitly. In this work, we propose a variant of a Loewner matrix-based interpolation algorithm that implicitly addresses possibly non-proper components of the system response. We evaluate the performance of the suggested approach by comparing against recently-developed explicit algorithms for which we propose a linearized Navier-Stokes model with a significant non-proper behavior as a benchmark example.

Keywords: Distributed parameter systems, Reduced order modeling, Identification
Abstract: Source term estimation (STE) is a field of growing interest in the context of air pollution, both for people living in urban areas and for decision makers. Thus retrieving maps of sources of pollution in an urban context is a necessity. Since urban pollution mainly depends on car traffic conditions, it is important to develop fast estimation methods to quickly and enough accurately identify highly-polluting vehicles. The challenge is high since the problem requires the inversion of distributed models defined on a 3D heterogeneous domain including complex obstacles. This paper proposes an estimation method based on a flexible least squares-radial basis function-finite differences (LS-RBF-FD) reduced model of an advection-diffusion PDE on 3D heterogeneous domains representing complex urban areas. The STE problem is solved by using an adjoint-based method relying on the reduced model to effectively estimate pollutant sources given a limited number of measurements. The paper provides preliminary results demon- strating the potential of the proposed approach.

Keywords: Robust control, Sliding mode control, Stability of nonlinear systems
Abstract: For many robot manipulators tasks, the main objective consists in following accurately position and velocity time varying trajectories. Sliding Mode Control (SMC) techniques allow exact position tracking despite a poor knowledge of the robot model and in the presence of external perturbations. In the last years fixed time stability has become an area of great interest, where one of the main problems to be solved is the possible appearance of singularities. The current contribution introduces a singularity free fixed time SMC scheme for position and velocity tracking of robot manipulators in the presence of external perturbations. The control scheme avoids singularities by using linear sliding mode (LSM) surfaces, which are yielded to zero in a predefined fixed time. Although the employment of LSM allows to avoid singularities, the tracking errors tend to zero only asymptotically once the sliding surface is reached. Simulation results show that this does not represent a big drawback since tracking errors tend to zero even in the presence of external perturbations as foreseen in theory.

Keywords: Robust control, Stochastic control, Predictive control for nonlinear systems
Abstract: Robust and stochastic optimal control problem (OCP) formulations allow a systematic treatment of uncertainty, but are typically associated with a high computational cost. The recently proposed zero-order robust optimization (zoRO) algorithm mitigates the computational cost of uncertainty- aware MPC by propagating the uncertainties separately from the nominal dynamics. This paper details the combination of zoRO with the real-time iteration (RTI) scheme and presents an efficient open-source implementation in acados, utilizing BLASFEO for the linear algebra operations. In addition to the scaling advantages posed by the zoRO algorithm, the efficient implementation drastically reduces the computational overhead, and, combined with an RTI scheme, enables the use of tube-based MPC for a wider range of applications. The flexibility, usability and effectiveness of the proposed implementation is demonstrated on two examples. On the practical example of a differential drive robot, the proposed implementation results in a tenfold reduction of computation time with respect to the previously available zoRO implementation.

Keywords: Robust control, Constrained control, Predictive control for linear systems
Abstract: In this work, we develop a method based on robust control techniques to synthesize robust time-varying state-feedback policies for finite, infinite, and receding horizon control problems subject to convex quadratic state and input constraints. To ensure constraint satisfaction of our policy, we employ (initial state)-to-peak gain techniques. Based on this idea, we formulate linear matrix inequality conditions, which are simultaneously convex in the parameters of an affine control policy, a Lyapunov function along the trajectory and multiplier variables for the uncertainties in a time-varying linear fractional transformation model. In our experiments this approach is less conservative than standard tube-based robust model predictive control methods.

Keywords: Robust control, Linear systems, Optimal control
Abstract: This paper provides a quadratic approach to rejection of amplitude-bounded input disturbances for single-input linear discrete-time systems. Control specification is that a quadratic form decreases along the state trajectories when a quadratic constraint on the state is violated. All the state-feedback controllers that satisfy the specification are parameterized using the solution of the Riccati equation in cheap optimal control. The robustness of the controllers represented by the maximum allowable amplitude of disturbances is not uniform over the state space and proportional to the constrained value. In special cases, the optimal performance is represented using system parameters such as unstable zeros of the plant.

Keywords: Robust control, LMI's/BMI's/SOS's, H2/H-infinity methods
Abstract: In this work we provide a data-based controller design method for uncertain input-saturated linear systems, where conditions based on linear matrix inequalities (LMIs) are exploited to guarantee robust global and regional closed-loop stability. The method is tested in simulation on a realistic case-study: the trade-off between performances and the region of validity of the established stability conditions is analysed.

Keywords: Robust control, Optimization, Aerospace
Abstract: Redundancy actuation systems are widely applied in the fields of aviation and aerospace. This paper establishes a nonlinear dynamic model of the dual-redundancy actuation system driven by electro-mechanical actuators, involving control constraints and unmatched bounded perturbation. We next focus on the reset control of the dual-redundancy actuation system and raise a robust control problem, which can be transformed into a specified optimal controller design problem theoretically. To acquire the optimal controller, we propose a modified robust adaptive dynamic programming approach. Using multiple critic neural networks, a simultaneous approximation of the value function and the controller are achieved, and all the weight parameters can be estimated through an optimization framework, which is feasible guaranteed via welldefined basis functions. Finally, a numerical example from the dual-redundancy actuation system is presented to illustrate the derived controller.

Keywords: Stochastic control, Optimal control, Predictive control for linear systems
Abstract: Scenario reduction algorithms can be an effective means to provide a tractable description of the uncertainty in optimal control problems. However, they might significantly compromise the performance of the controlled system. In this paper, we propose a method to compensate for the effect of scenario reduction on stochastic optimal control problems for chance-constrained linear systems with additive uncertainty. We consider a setting in which the uncertainty has a discrete distribution, where the number of possible realizations is large. We then propose a reduction algorithm with a problem-dependent loss function, and we define sufficient conditions on the stochastic optimal control problem to ensure out-of-sample guarantees (i.e., against the original distribution of the uncertainty) for the controlled system in terms of performance and chance constraint satisfaction. Finally, we demonstrate the effectiveness of the approach on a numerical example.

Keywords: Stochastic systems, Signal processing
Abstract: We present a principled study on defining Gaussian processes (GPs) with inputs on the product of directional manifolds. A circular kernel is first presented according to the von Mises distribution. Based thereon, the hypertoroidal von Mises (HvM) kernel is proposed to establish GPs on hypertori with consideration of correlated circular components. The proposed HvM kernel is demonstrated with multi-output GP regression for learning vector-valued functions on hypertori using the intrinsic coregionalization model. Analytic derivatives for hyperparameter optimization are provided for runtime-critical applications. For evaluation, we synthesize a ranging-based sensor network and employ the HvM-based GPs for data-driven recursive localization. Numerical results show that the HvM-based GP achieves superior tracking accuracy compared to parametric model and GPs of conventional kernel designs.

Keywords: Filtering, Observers for nonlinear systems, Linear parameter-varying systems
Abstract: In this paper, we propose a mixed filter for discrete-time nonlinear dynamical systems whose uncertainties are composed of both deterministic and stochastic terms. In practice, such mixed composition of uncertainties may appear when assimilating measured data and approximating models. The unknown-but-bounded terms are here represented by zonotopes, which are efficient representations of centrally symmetric convex polytopes. In turn, the stochastic terms are represented by Gaussian random vectors (GRVs) which address confidence regions with high probability. The proposed state estimator is based on linearized models, using the quasi-linear parameter-varying (LPV) approach. The effectiveness of our proposal is illustrated in two case studies.

Keywords: Filtering, Servo control, Mechatronics
Abstract: In wafer scanners, moving average and moving standard deviation constitute time-domain measures that quantify scanning stage performance in terms of overlay and imaging. Though for control design a frequency-domain interpretation would be desirable to have, for moving standard deviation such an interpretation is not easily found. As a practical solution, an analytical expression for the response to single sinusoidal input will be derived. For data from an industrial scanning stage the usefulness of such an expression and the insights obtained from it will be discussed.

Keywords: Stochastic filtering, Nonlinear system identification, Biological systems
Abstract: In this study, we address the intricate challenge of reconciling environmental sustainability with economic viability within wastewater treatment plants (WWTPs). Our primary objective is to minimize fossil energy consumption and reduce nitrogen concentrations. Current controllers struggle to adapt to fluctuating electricity prices and the variable conditions within WWTPs. While Model Predictive Control and Dynamic Programming offer promising control strategies, their effective deployment hinges on the availability of a robust system dynamics model. To address the stochastic and nonlinear nature of WWTP processes, we introduce a stochastic model and estimation method combining a Monte Carlo Sequential smoothing algorithm with a Stochastic Expectation Maximization method. The proposed methodology results in accurate 24-hour confidence interval predictions, outperforming the conventional estimation method, Prediction Error Minimization (PEM), offering a reliable model for control of WWTPs.

Keywords: Stochastic filtering, Nonlinear system theory, Aerospace
Abstract: This paper presents an efficient approximation for the second-order extended Kalman filter (SEKF) for nonlinear systems possessing a dominant direction of nonlinearity, which we call the directional second-order extended Kalman filter (DSEKF). Under certain assumptions, it is shown that the second-order terms in the standard SEKF can be accurately approximated with a single function evaluation. The DSEKF approximation addresses some of the drawbacks of the standard SEKF - namely, that the SEKF requires deriving the second-order state rates for the system, and requires integrating a large amount of additional terms on top of the first-order extended Kalman filter (EKF). The resulting algorithm is a promising and efficient alternative to sampling-based nonlinear filtering methods such as the unscented Kalman filter (UKF). The DSEKF can also be easily added onto existing operational systems that already use the EKF.

Keywords: Signal processing, Sensor and signal fusion, Autonomous systems
Abstract: Event cameras are bio-inspired vision sensors with much lower latency, higher dynamic range, and much lower power consumption than standard cameras. This talk will present current trends and opportunities with event cameras, ranging from robotics to virtual reality and smartphones, as well as open challenges and the road ahead.

Keywords: Applications in neuroscience, Discrete event systems, Filtering
Abstract: Event Cameras provide a unique new sensor modality for robotics and automation sensing. The high-temporal resolution and high dynamic range of event sensors are synergistic with the low temporal resolution and low-dynamic range of classical machine vision sensors offering the potential to augment existing vision systems in the future. Furthermore, the strengths of the event camera offer opportunities to undertake new vision sensing tasks that were either impossible or very difficult with existing machine vision. The applicability of existing computer vision algorithms to event camera data is marginal at best and in order to exploit their potential there is a need for the development of signal processing and filtering algorithms targeted their specific data characteristics. Since the sensor itself is inherently spatio-temporal, tools from system theory are highly applicable and lead to significant real-world performance gains. In this talk I will demonstrate some of the algorithms developed in the System Theory and Robotics lab for asynchronous event camera data processing and provide some insight into the development of these algorithms based on exploiting classical linear and stochastic filter paradigms from systems theory.

Keywords: Robotics, Emerging control applications, Emerging control theory
Abstract: Event cameras are revolutionising computer vision. They are at the forefront of event-based sensing technology and open new possibilities for robotics and control. They raise new scientific questions about the potential of event-based information. How to close the loop between event sensors and event actuators ? How to design event-based control laws that interconnect events to events ? The goal of this tutorial is to raise the awareness of the control community for those questions and to discuss the opportunities and challenges of this new technology for control.

Keywords: Identification, Stability of linear systems, Linear systems
Abstract: Machine Learning (ML) and linear System Identification (SI) have been historically developed independently. In this paper, we leverage well-established ML tools - especially the automatic differentiation framework - to introduce SIMBa, a family of discrete linear multi-step-ahead state-space SI methods using backpropagation. SIMBa relies on a novel Linear-Matrix-Inequality-based free parametrization of Schur matrices to ensure the stability of the identified model.

We show how SIMBa generally outperforms traditional linear state-space SI methods, and sometimes significantly, although at the price of a higher computational burden. This performance gap is particularly remarkable compared to other SI methods with stability guarantees, where the gain is frequently above 25% in our investigations, hinting at SIMBa's ability to simultaneously achieve state-of-the-art fitting performance and enforce stability. Interestingly, these observations hold for a wide variety of input-output systems and on both simulated and real-world data, showcasing the flexibility of the proposed approach. We postulate that this new SI paradigm presents a great extension potential to identify structured nonlinear models from data, and we hence open-source SIMBa on https://github.com/Cemempamoi/simba.

Keywords: Identification, Linear systems
Abstract: Data driven system identification is the technique for learning models from input/output data. To increase the robustness of the model estimation, prior knowledge can be incorporated, the so-called gray-box identification. In finite impulse response (FIR) models, prior knowledge of the process under investigation can be introduced by regularization. In the regularization term basic impulse response characteristics such as smoothness and exponentially decaying behavior can be incorporated. For estimation of time-delay systems, the novel impulse response and time-delay preserving (IRDP) regularization matrix is proposed. In this contribution this method is extended to the estimation of multiple input single output (MISO) processes and is compared to other state-of-the-art approaches. A linear process with four inputs and different input dynamics and time-delays is investigated. The focus of the evaluation is placed on model quality, time-delay estimation, and computation time. The simulation results point out the superiority of the novel regularization approach in comparison to state-of-the-art methods.

Keywords: Identification, Nonlinear system identification, Energy systems
Abstract: Identification of lithium-ion (Li-ion) battery models is essential for enhancing the operation of electrical vehicles. This paper develops a novel approach for estimating the equivalent circuit model (ECM) of Li-ion batteries and reconstructing the open-circuit voltage (OCV) and state of charge (SOC) relationship. We formulate the OCV-SOC relation as a piecewise affine (PWA) function and estimate its coefficients and the Markov parameters (impulse response) of the ECM via L1-regularized least squares. The state space model of the ECM is derived through the Ho-Kalman algorithm. Experiments with simulated and real-life battery data demonstrate the method's effectiveness and advantages with respect to the state of the art.

Keywords: Identification, Statistical learning, Optimization
Abstract: We consider the problem of identifying linear time invariant systems using regularization schemes, and address the fact that generally the mean value of the corresponding parameter prior is set to zero. We thus consider the scenario where it is beneficial to use a prior with nonzero-mean, where this mean moreover depends on some hyperparameters. We show how to construct such priors and do hyperparameter tuning by marginal likelihood, and since a parameter dependent mean may slow down optimization, we also derive an efficient and stable way of treating them, leading to an overall scheme whose leading order numerical complexity is the same as in the case where the prior mean is zero. The proposed method thus allows including new types of external information in the prior, and we exemplify how this extension can improve the existing regularization techniques.

Keywords: Identification
Abstract: Standard identification methods give biased parameter estimates when recorded signals are corrupted by noise on both input and output sides. In previous papers it has been shown that the bias is significant in case the system is almost non-identifiable. This situation is investigated here for some general model structures.

Keywords: Predictive control for nonlinear systems, Nonlinear system identification, Neural networks
Abstract: The Koopman operator theory is a powerful tool for the linear analysis and control of nonlinear systems that lifts the nonlinear states into a higher dimensional linear space known as the Koopman space. The linear Koopman space provides an attractive approach for designing linear control strategies for nonlinear systems. However, a significant challenge arises because the Koopman states cannot be directly related to the actual states of the system. This discrepancy can complicate the design of cost functions and constraints for a model predictive controller. In this work, we introduce a novel Koopman representation combined with a training scheme that resolves this issue by defining auxiliary states that are injective and monotonic to the original states. We evaluate the effectiveness of the proposed scheme through numerical experiments.

Keywords: Predictive control for nonlinear systems, Optimal control, Optimization algorithms
Abstract: This paper proposes a control law that improves the energy efficiency of a quadrotor UAV. An optimal velocity hypothesis was followed, thanks to the correlation between ground velocity and total consumed energy. An explicit energy model is developed and employed within the trajectory optimization problems. The proposed approach comprises an optimization stage at which an energy-efficient trajectory is generated followed by a Nonlinear Model Predictive Controller that tracks the generated optimal position and velocity subtrajectories, while the open-loop optimal control problem used to generate the trajectory is dynamically constrained. The proposed control approach was extensively tested in simulation and compared to tracking obtained with standard NMPC, PID, and LQR feedback controllers. The preliminary results demonstrate the validation of our hypothesis and a noteworthy reduction in energy consumption when compared with the use of the other standard feedback controllers, a 36% of energy saving was achieved in some cases.

Keywords: Predictive control for nonlinear systems, Autonomous robots, Robotics
Abstract: The ability to discern human intentions from brain signals has opened the possibility of leveraging Brain-Machine Interfaces (BMIs) for the control of robotic devices, especially benefiting individuals with severe motor disabilities. In this work, we present a novel approach for navigating a semiautonomous wheelchair towards targets generated by a BMI, all while ensuring collision avoidance. Our approach employs Nonlinear Model Predictive Control (NMPC) for real-time trajectory generation in unknown and dynamic environments. The empirical results obtained from real-world experiments clearly demonstrate the advancements of our solution over current state-of-the-art techniques. Our implementation is proven to outperform well-established methods in terms of both smoothness and alignment with the user's intended behavior.

Keywords: Predictive control for nonlinear systems, Automotive, Optimal control
Abstract: This paper presents a nonlinear model predictive control strategy for automotive fuel cell system operation. The system-level air-path modeling approach, widely adopted in control-oriented studies, is extended by the critical aspect of membrane hydration. The control problem formulation reflects the task of dynamic power tracking and efficiency-optimized actuation of the peripheral components as well as adherence to system constraints to avoid harmful operation. This is combined with an analysis of the design parameters sampling time, integration scheme, and prediction horizon for efficient transcription of the optimal control problem. Closed-loop simulation results, conducted using a sampling rate of 8 ms, the standard fourth-order explicit Runge-Kutta method with one integration step and a horizon length of 25, successfully meet the control objectives.

Keywords: Robotics, Predictive control for nonlinear systems
Abstract: In robotics, automating surface following is desirable for applications such as spray painting, inspection, and surface finishing. However, current surface following approaches focus on in-contact applications, where the robot is restricted to operate within a slow dynamic range. We introduce an approach tailored for contactless surface following, which leverages Model Predictive Control (MPC) to better exploit the full dynamic range of the robot while taking its acceleration limits into account. Our formulation introduces a surface model based on Radial Basis Function Networks (RBFN), and we show that when it is combined with local sensing and a surface estimator, it achieves significantly better performance compared to state-of-the-art approaches which rely on a local quadratic model. The approach was validated through extensive simulations, and it was found to reliably compute feasible control inputs within real-time. Through this research we aim to enable less conservative surface-following behaviour and bring MPC closer to real-life industrial applications in robotics.

Keywords: Optimization algorithms, Optimization, Randomized algorithms
Abstract: This paper addresses a unified convergence analysis for a large family of stochastic gradient projection algorithms for dealing with constrained finite sum convex problems, with a smooth objective function satisfying a strong convexity condition and possibly nonsmooth functional constraints. At each iteration, the algorithm takes an optimal gradient step based on a stochastic unbiased estimate of the objective function aiming to minimize it and then a feasibility step reducing the infeasibility associated with randomly observed constraints. Our stochastic gradient estimate can take diverse forms (e.g., Stochastic Gradient Descent (SGD), Stochastic Average Gradient Acceleration (SAGA), Loopless-Stochastic Variance Reduced Gradient (L-SVRG)). We conduct a convergence analysis of the proposed unified stochastic gradient projection algorithm under a diminishing stepsize, resulting in sublinear convergence rates, which are optimal for stochastic gradient methods within this problem class. Numerical evidence supports the effectiveness of our approach.

Keywords: Optimization algorithms, Optimization
Abstract: The weighted linearization is a generalization of the first-order Taylor approximation where the computation of the Jacobian matrices at the point of interest is replaced by the computation of the integral of the Jacobian matrix function by a weighting function that expresses how much different parts of the domain should be taken into account during the linearization. This paper blends the weighted linearization with the existing gradient descent (GD) method to develop a novel optimization technique named weighted gradient descent (WGD). The WGD is shown to outperform the GD in terms of mean absolute error, given an appropriate tuning of the WGD hyper-parameters, when applied to various nonlinear functions that are multi-modal in nature, thus exhibiting several optima.

Keywords: Control over communication, Control over networks, Optimization
Abstract: We study a linear quadratic regulation problem with a constraint where the control input can be nonzero only at a limited number of times. Given that this constraint leads to a combinational optimization problem, we adopt a greedy method to find a suboptimal solution. To quantify the performance of the greedy algorithm, we employ two metrics that reflect the submodularity level of the objective function: The submodularity ratio and curvature. We first present an explicit form of the optimal control input that is amenable to evaluating these metrics. Subsequently, we establish bounds on the submodularity ratio and curvature, which enable us to offer a practical performance guarantee for the greedy algorithm. The effectiveness of our guarantee is further demonstrated through numerical simulations.

Keywords: Electrical machine control, Intelligent systems, Optimization
Abstract: Robotic and automotive platforms are rapidly expanding in features and are incorporating more and more electric motor components. Consequently, the energy efficiency of motor control systems emerges as a major design challenge. The process of formulating and fine-tuning specialized speed regulation strategies for each application becomes progressively more laborious and expensive. A reinforcement learning agent specialized in electrical motor dynamics, capable of generalizing across a wide range of possible end-use applications, presents a promising and convenient solution. In this article, we introduce a novel design of a reinforcement learning agent, grounded in time series analysis, intended for application-agnostic electric motor control that optimizes both speed regulation and energy efficiency. Trained on the motor’s internal dynamics, the agent provides operating point-specific control inputs, eliminating the need for manual tuning and application system-identification. Compared to application tuned classical control methods, the agent exhibited on-par or improved speed regulation performance and demonstrated advanced capability to save energy, showcasing its potential for future applications.

Keywords: Quantized systems, Sampled data control, Optimization
Abstract: The uniform quantization effects present in the implementation of numeric regulators introduce undesired tracking errors, even though their continuous-time counterparts can ensure ideal steady-state response. Without perturbing the transient response, the state realization of the regulator can be scaled to reduce the influence of the steady-state artifacts. A main theoretical contribution is thus proposed, based on two complementary aspects. The starting point is given by an analytical bound of the quantization error. On one hand, this guaranteeable bound is minimized by the existence of an optimally scaled similarity matrix for the Jordan form of the closed-loop state matrix. On the other hand, a balancing scheme for the numeric regulator further reduces the quantization effects for a predefined hardware configuration. Mathematical guarantees to enforce said properties are then presented, developing sufficient conditions. Finally, the proposed method is illustrated on a case study which demonstrates the non-conservative nature of the optimized bound in comparison to the default value from the characterization theorem.

Keywords: Autonomous systems, Cooperative autonomous systems, Autonomous robots
Abstract: This research paper addresses the topics of the environment perception domain to realise the solution for connected autonomous mobility by using simulation softwares and real life sensors in tandem. The camera and lidar sensor simulation is performed for a specific scenario arising in the environment and the simulation results, sensor readings and the binary occupancy grids received from sensor simulations are analysed. The RealSense Depth Camera is used to perform visual mapping of the static environment for static obstacle perception and map generation whereas the SICK 2D lidar is used to create a dynamic probabilistic occupancy grid for perceiving the dynamic obstacles in the environment. The results from the sensors in simulation and real life are compared, analysed and validated. The dynamic probabilistic occupancy is used as a foundation to further develop an occupancy prediction model which predicts the future occupancy of any dynamic obstacle based on its velocity and direction of motion. Furthermore, a framework is conceptualised for the Vehicle to Everything (V2X) communication system which includes the identification and determination of the essential communication infrastructure, type of data, recipients, rate of data transfer and ROS communication nodes.

Keywords: Autonomous systems, Optimization, Maritime
Abstract: Following the recent upgrade of the propulsion plant configuration for the milliAmpere1 passenger ferry prototype, a model-based controller pipeline suitable for all the milliAmpere1 speed ranges is proposed and evaluated. Some well-known methods for force allocation and reference model systems are implemented together with a nonlinear model-based motion controller, and a comparison study for different combinations is carried out to define the best solution for this application. Considering low-speed operations, the efficiency of three force allocation solutions and three reference models with two possible thruster configurations are investigated. The resulting controllers are evaluated with five performance metrics. For higher-speed operations, a solution with a reference model and a force allocation is presented and investigated. The controllers have been tested via numerical simulations, and based on the performance metrics, indications for the best design options are provided.

Keywords: Autonomous systems, Stability of nonlinear systems, Automotive
Abstract: This paper presents a RADAR-Based, vehicle lateral dynamics control algorithm that ensures a safe pull-over of autonomous racing vehicles to the roadside barriers in the event of localization failures. The position and curvature of the roadside barriers are estimated from RADAR measurements through a Total Least Squares algorithm and fed to a Linear-Quadratic-Regulator (LQR), which outputs the steering commands to the vehicle. As the estimates accuracy varies with the distance to the barriers, the proposed algorithm controls the car at a target distance which is dynamically adjusted based on the confidence interval of the barrier distance estimate. The convergence of the resulting closed-loop, nonlinear system to a minimum safe distance to the barrier is proven through Lyapunov stability theory. We consider the setup based on the Dallara AV-21, a fully autonomous racing car competing in the Indy Autonomous Challenge. Simulations on the Las Vegas Motor Speedway track demonstrate the emergency controller's effectiveness, offering a robust safety solution for racing cars in case of localization losses.

Keywords: Autonomous systems
Abstract: This paper introduces a safe force/position tracking control strategy designed for Free-Floating Mobile manipulator Systems (MMSs) engaging in compliant contact with planar surfaces. The strategy uniquely integrates the Control Barrier Function (CBF) to manage operational limitations and safety concerns. It effectively addresses safety-critical aspects in the kinematic as well as dynamic level, such as manipulator joint limits, system velocity constraints, and inherent system dynamic uncertainties. The proposed strategy remains robust to the uncertainties of the MMS dynamic model, external disturbances, or variations in the contact stiffness model. The proposed control method has low computational demand ensures easy implementation on onboard computing systems, endorsing real-time operations. Simulation results verify the strategy’s efficacy, reflecting enhanced system performance and safety.

Keywords: Autonomous systems, Observers for nonlinear systems, Lyapunov methods
Abstract: This paper addresses a problem related to sensor attacks in autonomous vehicles. We proposes an approach that integrates a path following control framework with a novel nonlinear observer design in the context of autonomous vehicle systems. This tailored approach aims to effectively detect and mitigate sensor attacks, ensuring stable path tracking in the context of path-following dynamics. By leveraging the proposed observer's capabilities, we accurately estimate both the state and magnitude of the attacks. Through a comprehensive series of simulation studies, we demonstrate the practicality and effectiveness of our proposed methodology in enhancing the resilience of autonomous systems against sensor attacks.

Keywords: Sliding mode control, Lyapunov methods, Observers for nonlinear systems
Abstract: The differentiation of noisy signals using the family of homogeneous differentiators is considered. It includes the high-gain (linear) as well as robust exact (discontinuous) differentiator. To characterize the effect of noise and disturbance on the differentiation estimation error, the generalized, homogeneous L2-gain is utilized. Analog to the classical Lp-gain, it is not defined for the discontinuous case w.r.t. disturbances acting on the last channel. Thus, only continuous differentiators are addressed. The gain is estimated using a differential dissipation inequality, where a scaled Lyapunov function acts as storage function for the homogeneous L2 supply rate. The fixed differentiator gains are scaled with a gain-scaling parameter similar to the high-gain differentiator. This paper shows the existence of an optimal scaling which (locally) minimizes the homogeneous L2-gain estimate and provides a procedure to obtain it. Differentiators of dimension two are considered and the results are illustrated via numerical evaluation and a simulation example.

Keywords: Sliding mode control, Robust control, Linear systems
Abstract: This paper proposes a novel approach for the design of stabilizing sliding manifolds for linear systems affected by model uncertainties and external disturbances. In classical sliding mode control approaches, rejecting model uncertainties and external disturbances often relies on designing a discontinuous control law with a suitable gain. Specifically, the greater the uncertainty, the larger the control gain. However, this approach might be detrimental to the plant. Instead, the proposed technique deals with this problem by focusing on the design of a suitable sliding manifold, where stability is guaranteed despite model uncertainties. This approach exhibits several benefits such as not needing any further identification process and designing a smaller control gain.

Keywords: Sliding mode control, Uncertain systems, Stability of nonlinear systems
Abstract: We consider a super-twisting sliding-mode controller with an integral sliding variable. The system is subject to time- and state-dependent perturbations. Our stability analysis yields an estimate for the region of attraction as well as bounds for the control signal and the sliding variables. We demonstrate the results with a numerical example.

Keywords: Flexible structures, Sliding mode control, Robotics
Abstract: This paper proposes a functional observer-based sliding mode control for position control of a Single-Link Flexible Manipulator (SLFM). The proposed control considers the unmodelled system dynamics as uncertainty and aims to achieve position control. The proposed control scheme is designed considering the reduced order dynamics. A functional observer is used to directly compute a sliding function and the control signal, which guarantees the system's robustness and stability. The proposed control scheme is validated for large-order ordinary differential equation (ODE) model of the SLFM using numerical simulations.

Keywords: Sliding mode control, Transportation systems
Abstract: The stabilization of first and second-order systems in the presence of disturbances has been investigated in this paper. A novel sliding variable is designed by utilizing the Gauss error and arctangent functions. This results in a new control rule that is characterized by its robustness and ease of fixed-time stability implementation. Then, sliding-mode control is applied to the global robust finite-time stabilization of a class of uncertain nonlinear second-order systems. In the simulation, every result is shown. The proposed controller is applied for lateral control of autonomous vehicles. The effectiveness of the suggested method for controlling an autonomous vehicle while into account disturbances is evaluated using a nonlinear simulation.

Keywords: Uncertain systems, Energy systems, Observers for linear systems
Abstract: Lithium-ion batteries are currently used in numerous applications, for example in electric vehicles and energy storage systems. Accurately estimating the dynamic behavior is crucial to safely and efficiently charge and discharge the battery cells. This can be done with the help of stochastic or interval based identification routines which require an accurate state estimation. In previous works, we used an interval observer based on a Luenberger observer structure to estimate the lower and upper bounds of the state variables. However, with this approach it was necessary to use specified input current profiles to charge or discharge the battery cells, otherwise, the estimation uncertainty increased over time. In this paper, we aim to estimate the state variables during system operation without using specified input current profiles. A promising approach is a TNL observer, which was introduced for uncertain time-invariant systems in the literature, that provides multiple design degrees of freedom. The TNL observer is extended to an uncertain time-varying system model of lithium-ion batteries in this work. The time-varying part is herein considered with two different approaches. At first, a nominal system model is considered where the time-varying part is treated as a measurement uncertainty and secondly it is considered using a polytopic representation of the system matrix. The TNL observer was successfully extended to the time-varying system model of a lithium-ion battery. The polytopic representation of the system matrix leads to more accurate estimation results in comparison to the nominal approach.

Keywords: Energy systems, Identification for control, Model validation
Abstract: Thermal models of lithium-ion cells play a critical role in monitoring temperature distribution within battery packs,sizing cooling systems, and predicting potential thermal runaway scenarios. Pouch-type lithium-ion cells offer high energy and power density solutions, however, they are less thermally and mechanically stable than their cylindrical counterparts. Pouch cells require appropriate containment structures to prevent excessive cell breathing that could alter electrical performance. Experiments done without such holding devices could conceal nominal cell behavior and lack repeatability. The holding structure, on the other hand, influences the heat dissipation of the cell and masks its temperature dynamics, making the characterization of its thermal properties challenging. This paper proposes a lumped-parameter thermal model and an experimental identification protocol aimed at extracting the parameters of interest, such as the cell thermal capacity, using temperature measurements collected both on the cell and fixture device components. The model is trained and validated under current profiles that are highly effective in exciting the system thermal dynamics.

Keywords: Energy systems, Optimal control, Uncertain systems
Abstract: In view of the advancements in microgrids technology, energy management plays an important role in optimizing energy resources and minimizing operational costs, however including distributed energy resources in the microgrids makes the system variability increase, then, an adequate control strategy is necessary for exploiting these resources appropriately. This study presents an intraday Energy Management System employing a Scenario-Based Model Predictive Controller in a multi-microgrid configuration. A hierarchical controller is proposed to minimize the economic cost of the deviations with respect to the day-ahead scheduling, in front of the uncertainty in renewable generation. The formulation guarantees a preset constraint violation probability, while simplifying the treatment of uncertainty. The results demonstrate that the approach outperforms the behavior of a deterministic Model Predictive Controller, reducing the economic costs by 16%. Moreover, it significantly reduces power deviations by up to 49%. This work highlights the potential of Scenario-Based Model Predictive Control as a promising tool for real-time multi-microgrid management, offering effective management of the uncertainty and guaranteeing probabilistic constraint satisfaction.

Keywords: Energy systems, Optimization algorithms, Adaptive systems
Abstract: Complex electrochemical processes of Li-ion batteries result in nonlinear and high-dimensional dynamics. With the increased presence in critical applications, there is a demand for advanced fast-charging strategies to reduce the charging time while maximizing the battery's lifespan. Fast charging is limited by several factors, such as elevated temperature, since they accelerate electrochemical aging and, in turn, result in increased lithium plating, higher mechanical stresses, and an increased growth rate of the solid-electrolyte interface layer. Here, we propose an aggressive but efficient charging strategy using an adaptive control strategy that learns the closed-loop system's Jacobian from input/output data and optimizes the response based on the learned dynamics. To avoid subjecting the cell to accelerated aging, we optimize the electrical current for minimum battery charge time while respecting constraints such as maximum cell temperature and voltage. The battery data was generated using the Doyle-Fuller-Newman (P2D) model with a thermal model to characterize the cell's thermal effects. Our optimized charging strategy is comprised of a hybrid (mixed continuous-discrete) solution that fully charges a 5Ah 21700 NMC-811 cylindrical cell, 66% faster than the recommended 0.3C constant-current constant-voltage strategy while respecting safety constraints, including a maximum voltage of 4.2V and a maximum temperature of 57 degC.

Keywords: Predictive control for linear systems, Energy systems, Uncertain systems
Abstract: Robust Optimal Control (ROC) with adjustable uncertainties has proven to be effective in addressing critical challenges within modern energy networks, especially the reserve and provision problem. However, prior research on ROC with adjustable uncertainties has predominantly focused on the scenario of uncertainties modeled as continuous variables. In this paper, we explore ROC with binary adjustable uncertainties, where the uncertainties are modeled by binary decision variables, marking the first investigation of its kind. To tackle this new challenge, firstly we introduce a metric designed to quantitatively measure the extent of binary adjustable uncertainties. Then, to balance computational tractability and adaptability, we restrict control policies to be affine functions with respect to uncertainties, and propose a general design framework for ROC with binary adjustable uncertainties. To address the inherent computational demands of the original ROC problem, especially in large-scale applications, we employ strong duality (SD) and big-M-based reformulations to create a scalable and computationally efficient Mixed-Integer Linear Programming (MILP) formulation. Numerical simulations are conducted to showcase the performance of our proposed approach, demonstrating its applicability and effectiveness in handling binary adjustable uncertainties within the context of modern energy networks.

Keywords: Automotive, Predictive control for linear systems, Game theoretical methods
Abstract: This work proposes a robust control strategy for an autonomous vehicle to overtake safely and comfortably a human-driven vehicle. The proposed scheme designs a collision-avoidance constraint setup that comprehensively coordinates dimension-based and velocity-dependent constraints to fulfil the safety criteria. A three-phase control framework is proposed for the overtaking task, subject to separate collision-avoidance constraints in lane changing, passing, and merging phases. Moreover, the proposed method utilises a Stackelberg game model to interactively involve the human-driven overtaken vehicle behaviours in the online optimisation loop. To further cope with uncertainties caused by the human driver, the optimisation is solved by a robust model predictive controller to guarantee the avoidance of collisions. Numerical case studies verify that the proposed framework is capable of overtaking not only a cooperative human-driven vehicle but also an uncooperative human-driven vehicle with safe and comfortable trajectories.

Keywords: Transportation systems
Abstract: This article introduces a statistical method to estimate trips origin and destination locations from a masked trip data set. The estimation method uses trip features, the graph of the network, and publicly accessible external information on the real-time congestion status to find the most probable trips origin and destination based on a Bayesian approach, Markov Chain rule, and rank aggregation method. A case study of Porto, Portugal assesses the performance of the statistical estimation method by comparing the estimated location with the centroids of reported locations and with the actual trip origin and destination. Despite the limitation of the available data, the method provides better estimates of trips origin and destination compared to the centroids of reported locations.

Keywords: Transportation systems, Cooperative autonomous systems, Constrained control
Abstract: This paper proposes a modification to a delay-based spacing policy for heterogeneous platooning applications. To this end, the approach to spatially track a reference speed profile time-shifted by a certain delay is augmented by a spatial offset that ensures safe operation over a full platoon mission, including standstill. A matching linear controller is derived to compensate for heterogeneous vehicle dynamics. By means of homogenization, the different dynamic ranges of platoon members are considered. Finally, further performance features are realized through constrained control, such as the restriction to a permissible acceleration range, limited catch-up speed and safe braking distances.

Keywords: Transportation systems, Predictive control for nonlinear systems, Safety critical systems
Abstract: Autonomous driving in urban scenarios has become more challenging due to the increase in Personal Light Electric Vehicles (PLEVs). PLEVs correspond mostly to electric devices such as gyropods and scooters. They exhibit varying velocity profiles as a result of their high acceleration capacity. Multiple hypotheses about their possible motion make autonomous driving very difficult, leading to the highly conservative behavior of most control algorithms. This paper proposes to solve this problem by performing a continuous risk assessment using a Fusion of Predictive Inter-Distance Profile (F-PIDP). Then a stochastic MPC algorithm performs effective risk management using the F-PIDP while taking into account adaptive constraints. The advantages of the proposed approach are demonstrated through simulations of multiple scenarios.

Keywords: Cooperative control, Automotive, Transportation systems
Abstract: In this paper, we present a framework for combined path and motion trajectory planning for the purpose of coordinating fully automated vehicles in confined sites. The path planning component utilizes a Monte-Carlo tree search approach for computing the vehicle paths and the motion trajectory component utilizes a two-stage optimization-based algorithm that optimizes the state and input trajectories for all vehicles while avoiding inter-vehicle conflicts. The motion trajectories are tracked by a low-level controller and both the path and motion trajectories are recomputed based on the feedback signals. The performance of the framework is validated through numerical simulations and results show both improved energy efficiency and productivity.