Keywords: Electrical power systems
Abstract: The electric power grid is sometimes described as the largest and most complex machine in the world, and it is not go ing to get any simpler going forward. Technologies such as automation and control, data and communication, today seen as welcome additional features to improve performance and efficiency of the power grid, will increasingly become critical building blocks of a secure, green, and digital system fulfilling the sustainability targets. The talk will give an overview of future trends and needs in the area, with some illustrative examples.

Keywords: Adaptive control, Nonlinear system theory, Complex systems
Abstract: In this paper, a PID-structured controller with time-varying gains is introduced using model-free adaptive control (MFAC) methodologies. The MFAC uses a control-oriented linearized data-model to produce control input only using input/output (I/O) data of the system. One of the data-model structures is full form dynamic linearization (FFDL), which considers the effect of a time-window of previous I/O in the linearized model. A specific I/O window length in FFDL leads to a MFAC controller whose structure is similar to discrete multivariable type PID. By manipulating the control objective function, however, similar PID-structured (PIDs) controller can be realized using a less sophisticated data-model exploiting the compact form dynamic linearization (CFDL) technique. The complexity of the new PIDsMFAC-CFDL and the one realized by MFAC-FFDL are compared in terms of the total number of adjustable parameters when dealing with MIMO systems. The controllers are also applied on a simulated model of a nonlinear MIMO three-tank system (3TS). The results demonstrate that the number of parameters to be tuned can decrease heavily by considering the new approach. In addition, PIDsMFAC-CFDL delivers a smooth transition toward the given reference with less error and less consumed energy.

Keywords: Adaptive control, Energy systems, Adaptive systems
Abstract: The transactive control paradigm enables a flexible electricity-consuming asset to offer its flexibility upstream by simply adapting its consumption profile in response to pricing signals. A consumer's response to prices over time is modeled through a so-termed flexibility function. However, a consumer's flexibility function needs to be adaptive to account for changes to the consumer's internal dynamics over time. This paper proposes an adaptive mechanism for price signal generation using a piecewise linear approximation of a flexibility function with unknown parameters. In this adaptive approach, the price signal is parameterized and the parameters are changed adaptively such that the output of the flexibility function follows a reference demand signal. This is guaranteed using the Lyapunov stability theorem. The proposed method does not require an estimation algorithm for unknown parameters, which eliminates the need for the persistency of excitation of signals, and consequently, simplifies practical adoption and deployment.

Keywords: Mechatronics, Adaptive control, Model/Controller reduction
Abstract: Electromechanical switching devices, such as solenoid valves, contactors, and relays, suffer from undesirable phenomena like clicking, mechanical wear, and contact bounce. Despite that, they are still widely used in industry due to their various economic and technical advantages. This has encouraged the development of controllers aimed at reducing the collisions that occur at the end of the switching operations. One of the most successful approaches has been the use of iterative techniques. However, these algorithms typically require a large number of operations to converge, which is definitely a clear drawback. This paper presents a strategy to improve the convergence rate of such controllers. Our proposal, which is based on the sensitivity of the control law with respect to the parameters, assumes that the performance of the system is more heavily affected by some parameters than others. Thus, by avoiding movements in the directions that have less impact, the search algorithm is expected to drive the system to near-optimal behaviors using fewer operations. Results obtained by simulation show significant improvement in the convergence rate of a state-of-the-art run-to-run feedforward controller, which demonstrates the high potential of the proposal.

Keywords: Stochastic control, Identification for control, Adaptive control
Abstract: We propose a tractable adaptive risk-sensitive optimal control framework tailored to uncertain nonlinear stochastic system dynamics. The architecture exhibits dual features meaning that the controller actively maintains a balance between exploitation and exploration. The problem statement is cast as an instance of Active Inference. Active Inference is an emerging framework in theoretical neuroscience that seeks to explain the behaviour of biological agents by practising inference on probabilistic graph models. The developed algorithm leverages a receding horizon strategy that simultaneously estimates the uncertain parameters of the dynamic system from past observations and designs controller parameters by predicting the future performance of the controlled system. The algorithm does not make use of the separation and certainty equivalence principles. We further show that for the special case of linearly parameterized controller and dynamics, the approach leads to a quadratic programming problem maintaining a manageable computational complexity. The capability and anticipated properties of the proposed algorithm are demonstrated on a simulated nonlinear system.

Keywords: Neural networks, Adaptive control
Abstract: Echo state network (ESN) implements an alternative paradigm called reservoir computing to train recurrent neural networks (RNNs), where internal weights are randomly generated and kept fixed, and only readout weights need to be trained, which greatly reduces the training complexity of RNNs. ESN not only facilitates the practical implementation of RNNs but also shows superior performance over fully trained RNNs across a range of applications. However, the conventional ESN suffers from the drawbacks of stringent conditions for weight convergence and slow convergence speed. This paper proposes a memory regressor extended learning method to update the readout weights of ESNs. By constructing and incorporating a generalized prediction error based on regressor extension and filtering, the capacity of ESN to utilize historical data can be greatly improved. In the discrete-time domain, it is proven that exponential convergence of readout weights is achieved under a condition termed interval excitation that is strictly weaker than the classical condition of persistent excitation. Simulation results on modeling a 10th-order nonlinear autoregressive moving-average (NARMA) system have revealed that the proposed approach accelerates weight convergence speed almost ten times higher compared to the conventional ESN.

Keywords: Process control, Optimal control, Adaptive control
Abstract: This paper presents an adaptive optimal control algorithm to minimize membrane fouling and energy to deal with model uncertainty and disturbances in the characteristics of the water to be filtered. The Membrane BioReactor under interest is operated at constant flux and the objective is to minimize energy requirements such that the quantity of water filtered over a given period of time tf equals p*. The algorithm is based on the iterative application of the optimal strategy initially proposed in [1] to account on input water characteristics changes. With such a modified control approach, it is shown in simulations that the ratios between filtration and backwash time periods adapt over the time to take account on unknown water input variations.

Keywords: Optimal control, Predictive control for nonlinear systems, Automotive
Abstract: This paper proposes a space-sampled Economic Model Predictive Control (EMPC) approach to jointly minimize total energy consumption of an electric vehicle (EV) and track both longitudinal velocity and path curvature reference trajectories. We consider a single-track vehicle model constrained to the range of accelerations +/-3 m/s2, and energy consumption is modelled explicitly including power losses of electric machines. Simulations with the high-fidelity simulator IPG CarMaker show the trade-off between energy consumption and reference tracking. Namely, results show how longitudinal velocity and acceleration control significantly impact energy consumption, whereas deviating from the path centerline mainly allows better velocity tracking.

Keywords: Optimal control, Process control, Chemical process control
Abstract: Dynamical models exist for the non-complex membrane filtration process which has enabled full scale optimal control development. Parallel pressure filtration systems do have similarities with membrane filtration, but lack models suitable for optimal control and they operate as multiple parallel units and must be treated as a part of a larger plant. A simplified optimal control concept is presented and compared with optimal control of membrane filtration. The optimal control does not use constant filter run cycle times as previous works on membrane filtration, but is based on true feedback control and is capable of finding the optimal run cycle time in varying conditions. Plant-wide control is also addressed in the simulated case example presented in the paper.

Keywords: Optimal control, Robotics, Predictive control for nonlinear systems
Abstract: Direct methods that transcribe an Optimal Control Problem (OCP) to a Nonlinear Program (NLP) have proven effective to solve OCPs. Flexibility in this transcription that can adapt online to a changing environment by adding or removing constraints or changing the discretization of the dynamics can benefit many applications such as motion planning in dynamic environments. This work presents AdaptiveNLP, a software framework that efficiently constructs NLP functions based on pre-computed derivative information and provides functionalities to modify the NLP problem structure with low overhead. This adaptability enables the user to discard constraints known to be inactive which reduces computation times. In Model Predictive Control (MPC), it also allows tailoring a specific MPC iteration's NLP to the environment at that time instance. An MPC example and an adaptive gridding example show the effective reduction of total computation time and the ability to refine the time-grid of an NLP to produce a sparse but highly accurate solution with little overhead, respectively.

Keywords: Optimal control, Distributed cooperative control over networks, Concensus control and estimation
Abstract: This paper studies a manipulator co-design problem of motors and motions for multiple tasks. To reduce computational burden and improve scalability as the number of tasks grows, this paper introduces a distributed co-design framework to handle the co-design process for all tasks in a distributed fashion. Moreover, this paper presents a distributed constrained optimization algorithm, which secures a unified set of design parameters for all tasks ultimately such that the total average of motor operation efficiency is optimized and the design constraints are satisfied across all tasks and motors. The distributed manner reduces the computational load by allowing each agent to solve co-design optimization solely for its designated task. A numerical simulation further verifies the proposed algorithm.

Keywords: Optimal control, Machine learning, Robotics
Abstract: Energy-optimal operation of robotic systems has gained high interest in both industry and science. We propose to fuse model predictive control and Bayesian optimization to plan minimum-energy trajectories for industrial robots that guarantee successful executions of the primary task. Particularly, parts of the predictive planner are learned using Bayesian optimization to account for the secondary, higher-level objective -- here energy minimization. The effectiveness of the proposed approach is underlined in simulation, where a reduction in energy consumption is observed while maintaining a high quality of task executions.

Keywords: Optimal control, Observers for linear systems, Linear systems
Abstract: This paper presents a method to design an optimal controller-observer pair for a continuous linear time-invariant system with respect to a quadratic cost. First, we propose a novel generalized method that makes this otherwise complex problem solvable within the linear optimal control framework. Then, we derive a solution approach based on the augmented Lagrangian method to handle the inherent structural constraints associated with the problem. Finally, we show the utility of the proposed method through a numerical example.

Keywords: Traffic control, Transportation systems, Modeling
Abstract: In this paper, we propose the METANET with service station (METANET-s) model, a second-order macroscopic traffic model that, compared to the classical METANET, incorporates the dynamics of service stations on highways. Specifically, we employ the (so-called) store-and-forward links to model the stop of vehicles and the possible queue forming in the process of merging back into the highway mainstream. We explore the capability of the METANET-s to capture well both traffic back propagation and capacity drops, which are typically caused by the presence of vehicles joining again the mainstream traffic from the service station. Therefore, capturing these effects is crucial to improving the model’s predictive capabilities. Finally, we perform a comparative analysis with the Cell Transmission Model with service station (CTM-s), showcasing that the METANET-s describes the traffic evolution much better than its first-order counterpart.

Keywords: Transportation systems, Cooperative control, Distributed cooperative control over networks
Abstract: This paper solves the platoon control of nonlinear connected autonomous vehicles under variable spacing policy as a formation problem. The main aim is to guarantee that each vehicle, despite wireless communication impairments, moves according to the imposed speed profile while following the desired spacing, adaptable to different traffic conditions. To this end, we first design a novel full-range nonlinear spacing policy and then, by leveraging formation control theory, a distributed controller ensuring CAVs platoon variable formation, despite the presence of heterogeneous communication time-delay. By exploiting the Lyapunov-Krasovskii approach, we derive a delay-dependent stability conditions that, expressed as a set of feasible Linear Matrix Inequalities, allows tuning control gains. Simulation results, carried out via MiTraS platform, disclose the effectiveness of the proposed solution.

Keywords: Transportation systems, Machine learning, Autonomous systems
Abstract: Autonomous Mobility-on-Demand (AMoD) systems are an evolving mode of transportation in which a centrally coordinated fleet of self-driving vehicles dynamically serves travel requests. The control of these systems is typically formulated as a large network optimization problem, and reinforcement learning (RL) has recently emerged as a promising approach to solve the open challenges in this space. Recent centralized RL approaches focus on learning from online data, ignoring the per-sample-cost of interactions within real-world transportation systems. To address these limitations, we propose to formalize the control of AMoD systems through the lens of offline reinforcement learning and learn effective control strategies using solely offline data, which is readily available to current mobility operators. We further investigate design decisions and provide empirical evidence based on data from real-world mobility systems showing how offline learning allows to recover AMoD control policies that (i) exhibit performance on par with online methods, (ii) allow for sample-efficient online fine-tuning and (iii) eliminate the need for complex simulation environments. Crucially, this paper demonstrates that offline RL is a promising paradigm for the application of RL-based solutions within economically-critical systems, such as mobility systems.

Keywords: Transportation systems, Machine learning, Optimal control
Abstract: Operators of Electric Autonomous Mobility-on-Demand (E-AMoD) fleets need to make several real-time decisions such as matching available cars to ride requests, rebalancing idle cars to areas of high demand, and charging vehicles to ensure sufficient range. While this problem can be posed as a linear program that optimizes flows over a space-charge-time graph, the size of the resulting optimization problem does not allow for real-time implementation in realistic settings. In this work, we present the E-AMoD control problem through the lens of reinforcement learning and propose a graph network-based framework to achieve drastically improved scalability and superior performance over heuristics. Specifically, we adopt a bi-level formulation where we (1) leverage a graph network-based RL agent to specify a desired next state in the space-charge graph, and (2) solve more tractable linear programs to best achieve the desired state while ensuring feasibility. Experiments using real-world data from San Francisco and New York City show that our approach achieves up to 89% of the profits of the theoretically-optimal solution while achieving more than a 100x speedup in computational time. We further highlight promising zero-shot transfer capabilities of our learned policy on tasks such as inter-city generalization and service area expansion, thus showing the utility, scalability, and flexibility of our framework. Finally, our approach outperforms the best domain-specific heuristics with comparable runtimes, with an increase in profits by up to 3.2x.

Keywords: Automotive, Optimal control, Autonomous systems
Abstract: Developing signal-free intersections, where connected automated vehicles (CAVs) for all OD (origin-destination) movements are appropriately guided to cross simultaneously, may significantly improve throughput and reduce fuel consumption. Naturally, vehicles in the intersection area are not bound to lanes; therefore, it is reasonable to consider the crossing area as a lane-free infrastructure for further improved exploitation. This paper proposes a joint optimal control approach for CAVs crossing signal-free and lane-free intersections. Specifically, the control inputs of all vehicles, comprising acceleration and steering angle, are optimized over a time-horizon by solving a single optimal control problem (OCP) based on the bicycle model of vehicle dynamics. The cost function includes proper terms to ensure smooth and collision-free motion, while also considering fuel consumption and desired-speed tracking, when possible. Appropriate constraints are designed to respect the intersection boundaries and ensure smooth vehicle movements towards their respective destinations. The defined OCP is solved numerically via an efficient Feasible Direction Algorithm (FDA), which is acceptably fast. A challenging demonstration example confirms the effectiveness of the suggested method.

Keywords: Traffic control, Transportation systems, Optimal control
Abstract: This paper deals with the optimal speed control of two platoons that share part of their freeway routes and need to perform merging and diverging procedures. The proposed control scheme has a centralized nature and is applied periodically by the platoons coordinator which receives in real time the state of the platoons and the traffic measurements on the network, based on which a traffic prediction is made. Based on this information, the coordinator applies specific optimal control algorithms to decide whether the merger is convenient or not and to compute the optimal speed profiles of the two platoons before the merging, during the shared journey and after the diverging phase.

Keywords: UAV's, Aerospace, Stability of nonlinear systems
Abstract: This paper tackles the problem of path-following control for fixed-wing UAVs in the presence of wind disturbances with stability guarantee constraints. Building upon our prior research, we continue to advance our novel predictive path-following algorithm, grounded in a qLPV model representing the 3D kinematics of fixed-wing UAVs. In this study, we have introduced additional stability guarantee constraints, all while retaining the high-efficiency path-following performance. This qLPV model, established through a velocity-based linearization strategy, enables us to implement offset-free MPC, known for its robustness against disturbances, and to solve a quadratic optimization problem at each time step, proving its efficacy in path-following applications. Furthermore, this representation permits the addition of few constraints to the optimization problem, ensuring stability without the necessity of solving complex offline LMIs. To evaluate the effectiveness of our algorithm, we tested it on a 24.6 kg aerobatic UAV, navigating through two scenarios with nine waypoints each. Our simulations began with a 3D kinematic model and progressed to a higher-fidelity one, showing strong performance with assured stability. We assume the proposed algorithm converges to the optimal solution, yet recent studies on unconstrained problems noted cases of suboptimal convergence.

Keywords: UAV's, Modeling, Stability of nonlinear systems
Abstract: This paper presents a novel VTOL UAV that owns a link connecting four rotors and a fuselage by a passive joint, allowing the control of the rotor's tilting angle by adjusting only the rotors' thrust. This unique structure contributes to eliminating additional actuators, such as servo motors, to control the tilting angles of rotors, resulting in the UAV's weight lighter and simpler structure. We first derive the dynamical model of the newly designed UAV and analyze its controllability. Then, we design the controller that leverages the tiltable link with four rotors to accelerate the UAV while suppressing a deviation of the UAV's angle of attack from the desired value to restrain the change of the aerodynamic force. Finally, the validity of the proposed control strategy is evaluated in simulation study.

Keywords: UAV's, Agents and autonomous systems, Distributed control
Abstract: This paper proposes a novel strategy for the coordination of a team of quadrotor unmanned aerial vehicles (UAVs) that has to reach a target area, while moving in an environment with obstacles. The strategy consists of a coordination phase, where the quadrotors assume a given formation, followed by a mission phase, where the UAV formation navigates to the target. In the latter phase, according to a leader-follower configuration, the leading agent receives from the ground station an obstacle-free trajectory to track, whereas the coordinated followers reconstruct and track their collision-free reference trajectories via a distributed consensus scheme. Finally, some simulation results are presented.

Keywords: Feedback linearization, UAV's, Constrained control
Abstract: Nonlinearity and dimensionality have always been computationally challenging problems when it comes to the online implementation of optimization-based control approaches, especially in the presence of disturbances. In this paper, we show how to alleviate the problem via a variable reformulation derived from differential flatness for a quadcopter vehicle. More specifically, we present a robust model predictive control design, to track a predefined trajectory of a quadcopter in the presence of disturbances. The synthesis procedure starts with a coordinate change mediated by the model's flatness property. In this new representation, the dynamics become linear in closed-loop at the price of more convoluted constraint expressions, which are usually disregarded in the literature or simple approximations are proposed. Subsequently, with a proper parameterization to portray the feasible domain, the trajectory tracking problem is transformed into a stabilization of a constrained linear time-invariant system under disturbances, which is then handled by a robust model predictive controller. Simulations and experimental results are presented to analyze and validate the proposed scheme.

Keywords: UAV's, Lyapunov methods, Aerospace
Abstract: We propose a motion planner for quadrotors implemented as a search on a graph constructed from robust positively invariant (PI) sets. We model the position error dynamics of the quadrotor in closed-loop with an onboard controller as a second-order system with polytopic uncertainty in the gains. In addition, we consider bounded attitude tracking errors and additive input disturbances. Using linear matrix inequalities (LMIs), we compute small ellipsoidal robust PI sets and large ellipsoidal inflated safe PI sets around positional setpoints where collision avoidance and input constraints are satisfied. We use the sets to construct a graph where the nodes are associated to position setpoints, and the edges are included if it is possible to transition from one node setpoint to the next one while satisfying constraints and avoiding collisions. The motion plan is obtained by selecting the sequence of active setpoints based on set membership conditions. The construction of the graph can be performed offline, while the online computation of the motion plan is simple and fast, as demonstrated by a Monte-Carlo simulation study in a cluttered indoor environment.

Keywords: UAV's, Predictive control for nonlinear systems
Abstract: This paper explores nonlinear model predictive control (NMPC) for an unmanned aerial vehicle (UAV) operating in icing conditions, simulated as asymmetric icing on the wings and icing on the propeller. First, a NMPC flight controller based on a nominal model is tested together with a disturbance observer to handle unmodelled effects. Second, we test a NMPC that includes the effect of asymmetric icing in the prediction model to explore how it affects its performance and robustness, and simulations are performed to compare this NMPC to the NMPC without icing knowledge, and to a conventional PID controller. The results show a clear improvement in the performance of the NMPC when the NMPC prediction model includes icing, as well as better performance and robustness in extreme icing asymmetry cases compared to the PID controller. Additional simulations were performed, indicating a significant degree of robustness.

Keywords: Hybrid systems, Identification, Switched systems
Abstract: This paper proposes a novel method to approximate a stable and unique limit cycle, as obtained e.g. from simulation of a nonlinear model or from experimental data, by a planar switching affine system (PSAS) while preserving the properties of uniqueness and stability. In contrast to existing literature, which formulates elaborate procedures based on system transformation to conclude on stability and uniqueness, this paper provides easy to check sufficient conditions directly formulated for the PSAS. Those conditions then serve as equality and inequality constraints in an optimization problem to determine the model parameters of the PSAS which best approximate the data points measured for the given limit cycle. It is shown that the sufficient conditions lead to the same results as formulated in literature, while a system transformation is not necessary. Efficiency, flexibility, and performance of the proposed method are demonstrated for a numeric example.

Keywords: Stability of hybrid systems, Nonlinear system theory, Computational methods
Abstract: Scaled graphs allow for graphical analysis of nonlinear systems, but are generally difficult to compute. The aim of this paper is to develop a method for approximating the scaled graph of reset controllers. A key ingredient in our approach is the generalized Kalman-Yakubovich-Popov lemma to determine input specific input-output properties of a reset controller in the time domain. By combining the obtained time domain properties to cover the full input space, an over-approximation of the scaled graph is constructed. Using this approximation, we establish a feedback interconnection result and provide connections to classical input-output analysis frameworks. Several examples show the relevance of the results for the analysis and design of reset control systems.

Keywords: Hybrid systems, Stability of hybrid systems, Aerospace
Abstract: This paper introduces a hybrid dynamical system methodology for managing impulsive control in spacecraft rendezvous and proximity operations under the Hill-Clohessy- Wiltshire model. We address the control design problem by isolating the out-of-plane from the in-plane dynamics and present a feedback control law for each of them. This law is based on a Lyapunov function tailored to each of the dynamics, capable of addressing thruster saturation and also a minimum impulse bit. These Lyapunov functions were found by reformulating the system’s dynamics into coordinates that more intuitively represent their physical behavior. The effectiveness of our control laws is then shown through numerical simulation.

Keywords: Hybrid systems, Mechatronics, Output feedback
Abstract: Reset controllers have demonstrated their effectiveness in enhancing performance in precision motion systems. To further exploiting the potential of reset controllers, this study introduces a parallel-partial reset control structure. Frequency response analysis is effective for the design and fine-tuning of controllers in industries. However, conducting frequency response analysis for reset control systems poses challenges due to their nonlinearities. We develop frequency response analysis methods for both the open-loop and closed-loop parallel-partial reset systems. Simulation results validate the accuracy of the analysis methods, showcasing precision enhancements exceeding 100% compared to the traditional describing function method. Furthermore, we design a parallel-partial reset controller within the Proportional–Integral–Derivative (PID) control structure for a mass-spring-damper system. The frequency response analysis of the designed system indicates that, while maintaining the same bandwidth and phase margin of the first-order harmonics, the new system exhibits lower magnitudes of higher-order harmonics, compared to the traditional reset system. Moreover, simulation results demonstrate that the new system achieves lower overshoot and quicker settling time compared to both the traditional reset and linear systems.

Keywords: Hybrid systems, Distributed cooperative control over networks, Lyapunov methods
Abstract: In this work we consider the problem of control under Signal Temporal Logic specifications (STL) that depend on relative position information among neighboring agents. In particular, we consider STL tasks for given pairs of agents whose satisfaction is translated into a set of setpoint output tracking problems with transient and steady-state constraints. Contrary to existing work the proposed framework does not require initial satisfaction of the funnel constraints but can ensure their satisfaction within a pre-specified finite time. Given a tree topology in which agents sharing a STL task form an edge, we show that the resulting control laws ensure the satisfaction of the STL task as well as boundedness of all closed loop signals using only local information.

Keywords: Stability of hybrid systems, Sampled data control, Control over communication
Abstract: This paper presnets a novel framework for periodic event-triggered control (PETC) coupled with dynamic quantization for linear systems. Unlike traditional time-driven control methods, our approach leverages event-based mechanisms to judiciously update control actions, thus minimizing computational load and network traffic. We introduce a two-level dynamic quantizer for encoding feedback information with a single bit, thereby enhancing resource efficiency. The proposed PETC mechanism decides the transmission instants based on the quantized output samples. The resulting system is modeled as a hybrid dynamical system to capture both continuous and discrete dynamics. Sufficient conditions for ensuring the stability of the closed-loop system are presented in the form of a linear matrix inequality. Through numerical simulations, we demonstrate that our approach captures the initial output within a finite time and significantly reduces data transmissions compared to traditional methods. This paper makes key contributions in the integration of dynamic quantization with PETC, leading to resource-efficient and stable control systems.

Keywords: Energy systems, Constrained control, Decentralized control
Abstract: We consider control of multiple stable first-order systems which have a control coupling described by an M-matrix. These agents are subject to incremental sector-bounded nonlinearities. We show that such plants can be globally asymptotically stabilized to a unique equilibrium using fully decentralized proportional integral anti-windup-equipped controllers subject to local tuning rules. In addition, we show that when the nonlinearities correspond to the saturation function, the closed-loop asymptotically minimizes a weighted 1-norm of the agents state mismatch. The control strategy is finally compared to other state-of-the-art controllers on a numerical district heating example.

Keywords: Energy systems, Predictive control for nonlinear systems, Optimization algorithms
Abstract: We study the dispatching of multi-modal energy systems (MMES) from a sequential market perspective based on hierarchical Model Predictive Control (MPC). In a sequential setting, an upper level determines the purchased electrical power from the day-ahead market, followed by a lower-level MPC responsible for the dispatching of the multi-energy system according to the continuous trading. Our case study consists in {an} MMES in Hanover, Germany, with electrical and heat demands as well as photovoltaic and wind energy generation, storage and coupling elements. We show that the hierarchical MPC solution can be embedded within the European market and German market area to provide a judicious dispatching of the MMES, also under imperfect uncertainty forecast. In particular, we discuss a reasonable choice for the prediction horizons and the effect of the forecast on the total incurring cost.

Keywords: Game theoretical methods, Energy systems
Abstract: Demand response has been a promising solution for accommodating renewable energy in power systems. In this study, we consider a demand response scheme within a distribution network facing an energy supply deficit. The utility company incentivizes load aggregators to adjust their pre-scheduled energy consumption and generation to match the supply. Each aggregator, which represents a group of prosumers, aims to maximize its revenue by bidding strategically in the demand response scheme. Since aggregators act in their own self-interest and their revenues and feasible bids influence one another, we model their competition as a network-constrained aggregative game. This model incorporates power flow constraints to prevent potential line congestion. Given that there are no coordinators and aggregators can only communicate with their neighbours, we introduce a fully distributed generalized Nash equilibrium seeking algorithm to determine the optimal bidding strategies for aggregators in this game. Within this algorithm, only estimates of the aggregate and certain auxiliary variables are communicated among neighbouring aggregators. We demonstrate the convergence of this algorithm by constructing an equivalent iteration using the forward-backward splitting technique.

Keywords: Hybrid systems, Differential algebraic systems, Modeling
Abstract: Multilinear time-invariant (MTI) models are a mathematical framework to represent and analyze high-dimensional nonlinear systems. Tensor decomposition techniques are used to improve computational efficiency and reduce storage effort. Applications of large-scale multi-energy systems can benefit from novel methodologies, such as modeling power systems using semi-explicit and fully implicit differential-algebraic equations. The multilinear modeling framework is embedded in a new MTI-toolbox suitable for multi-energy networked systems. The modeling framework and MTI-toolbox is demonstrated by an interconnected medium-size power system network, comprised of small three bus subsystems.

Keywords: Large-scale systems, Optimal control, Network analysis and control
Abstract: In this paper we provide an analytical solution to an H-two optimal control problem, that applies whenever the process corresponds to a uniformly damped network of masses and springs. The solution covers both stable and unstable systems, and illustrates analytically how damping affects the levels of achievable performance. Furthermore, the resulting optimal controllers can be synthesised using passive damped mass-spring networks, allowing for controller implementations without an energy source. We investigate the impact of both positive and negative damping through a small numerical example.

Keywords: Energy systems, Optimization
Abstract: Utilizing demand-side flexibility provides a viable, cost-efficient and low-carbon alternative to standard grid reinforcement. Energy hubs can provide such a flexibility by leveraging both electrical and thermal grids. This work proposes a novel pricing model for distribution tariffs to fulfill network-wide objectives, while incentivising energy hubs to participate in a demand side flexibility scheme. In the first step, we formulate the optimization of the hub network and propose a novel approach to quantify the flexibility potential of the hubs. The energy hubs then provide the operator with their projected load and flexibility potential for the next day. Subsequently, the operator designs day-ahead tariffs to utilize this flexibility. The tariff incorporates the objectives of the network operator, reduction of carbon emissions in this study, while minimizing the energy costs. Finally, the energy hubs respond to the tariffs using a receding horizon controller to minimize the energy cost over the next day by exploiting building flexibility and both grids. An extensive numerical study on an network of 3 hubs with buildings of different sizes shows that the proposed pricing model can significantly reduce the carbon emissions with a low energy cost trade-off.

Keywords: Energy systems, Optimal control
Abstract: This paper presents a direct transcription framework for optimising the operation of gyroscope-based inertial wave energy harvesters (WEH). In contrast to currently adopted procedures, we introduce a tailored optimal map as part of the total flywheel speed of the WEH, designed to maximise energy absorption. Since the target optimisation problem is infinite-dimensional, we derive a direct transcription process accordingly, exploiting tools from the field of moment-based theory. Following a formal derivation, the proposed framework is applied to a gyropendulum-based system, offering a numerical appraisal of the main characteristics underlying the methodology. We show that the procedure offered within this study is able to significantly enhance power absorption, hence contributing towards optimal energy-maximising operation of this family of systems.

Keywords: Energy systems, Uncertain systems, Robust control
Abstract: The dynamical behaviour of wave energy conversion systems (WECs) can be described in terms of the well-known Navier-Stokes equations which, being significantly complex, preclude their use in control design and performance assessment procedures. As such, WEC devices are virtually always modelled in terms of rather simplistic dynamical representations, aiming to produce models which are tractable both from a computational, and an analytical perspective. These models, nonetheless, are inherently affected by uncertainty, introduced by the set of small motion assumptions used to derive such simplistic representations. Deriving methodologies for a sensible quantification of this uncertainty is hence fundamental to understanding the effect of these modelling assumptions in the overall control design and performance assessment procedures. This paper presents a set of experimental tests conducted on a prototype system, locked at different equilibrium positions to account for different wetted surfaces, with the objective of characterising the uncertainty introduced by assuming small device motion within WEC modelling. The corresponding modelling mismatch is quantified in the frequency-domain, and a family of WEC models is generated by means of additive uncertainty. Leveraging the identified set of systems, numerical simulations are performed to show the potential impact of this uncertainty in the performance estimation of this prototype WEC system, for different irregular sea states.

Keywords: Energy systems, Emerging control applications, Optimal control
Abstract: The wave energy field is characterised by its continuous growth and development in research and technology. Over recent decades, different application issues have hindered the worldwide implementation of wave energy devices, and only a few have reached the commercialisation stage. Some of these inherent challenges have driven the creation of innovative wave energy system designs. In particular, active mechanical motion rectification (AMMR) is a novel proposal to rectify the energy flux in wave energy devices. The objective of active rectification is twofold. On the one hand, it increases the overall system efficiency by achieving a higher average output velocity in the generator. On the other hand, the AMMR introduces a new variable in the control design: A switching law to connect and disconnect the generator from the wave capture structure. Thus, the control design possesses two degrees of freedom, significantly increasing the complexity of the energy-maximising power take-off control problem.

In this paper, the problem of designing an optimal control philosophy for an AMMR-based wave capture system is addressed. The problem is solved by proving that, a separation principle applies, and that the optimal control solution over a fixed interval, is independent of the optimal switching sequence selection. To illustrate the utility of the analytical results, a numerical example for a flap-type wave energy converter, utilising an AMMR-based power take-off, is presented.

Keywords: Energy systems, Modeling, Linear systems
Abstract: Maximising energy output through advanced control strategies is pivotal for the economic viability of wave energy converters (WECs). However, most existing literature primarily focuses on theoretical case studies, where WECs are constrained to operate in a single degree of freedom (DoF). This simplification is made due to the added complexity of optimizing across multiple DoFs. In this study, we assess the necessity of incorporating multiple DoFs within the control framework, evaluating its effectiveness in a numerical simulation environment that replicates WEC performance across multiple DoFs. To provide a basis for comparison, we contrast the conventional PI controller with the innovative LiteCon controller. Our study reveals two key findings: (i) Single DoF control may suffice when the primary DoF of the power take-off system is accurately identified, and (ii) the straightforward LiteCon controller outperforms the traditional PI controller by a significant margin.

Keywords: Maritime, Power plants, Predictive control for nonlinear systems
Abstract: Wave energy, harnessed by wave energy converters (WECs), has the potential to significantly contribute to the renewable energy mix. To improve the commercial viability of WECs, the design of control strategies for maximizing the produced energy is vital. This work specifically focuses on energy maximizing control for oscillating-water-column (OWC) WECs, using a receding-horizon pseudospectral (RHPS) optimal control method. With pseudospectral control, the continuous time OWC energy maximizing optimal control problem is directly transcribed, by discretizing both state, and control, variables, into a finite-dimensional nonlinear program. Due to the importance of turbine performance, OWC control typically aims to maximize turbine efficiency, albeit ignoring the impact of rotational speed on hydrodynamic performance. With the RHPS optimal control approach developed in this paper, a better trade-off between turbine and hydrodynamic performance is achieved and, therefore, energy production is improved.

Keywords: Energy systems, Sliding mode control, Optimal control
Abstract: The development of effective control strategies is essential to advance the commercialisation of wave energy converters by maximising energy capture. This study employs a novel control approach integrating an optimal control term, based on so-called moments, with a tracking term rooted in second-order sliding mode control. The optimal term determines the control action to optimise energy conversion under nominal conditions and provides an optimal velocity reference. Subsequently, a Super-twisting algorithm is deployed to robustly track the optimal reference. However, challenges of integral windup may arise when employing the Super-twisting algorithm within the tracking loop under conditions of actuator saturation. To address this, the paper implements two anti-windup techniques aimed at mitigating the windup effect in the Super-twisting algorithm. Simulation results validate the effectiveness of the proposed modification to the main control strategy in dealing with saturated actuators.

Keywords: Control over networks, Delay systems, Automotive
Abstract: Delay injection attacks on nonlinear control systems may trigger instability mechanisms like finite escape time dynamics. The paper guards against such attacks by showing how a recursive algorithm for identification of nonlinear dynamics and delay can simultaneously provide parameter estimates for controller tuning and detection of delay injection in the feedback path. The attack methodology is illustrated using a simulated feedback linearized automotive cruise controller where the attack is disguised, but anyway rapidly detected.

Keywords: Agents networks, Communication networks, Optimization algorithms
Abstract: This paper focuses on the distributed online convex optimization problem with time-varying inequality constraints over a network of agents, where the agents collaborate to minimize the cumulative network-wide loss over time through local information exchange. To reduce communication overhead between the agents, we propose a distributed event-triggered online primal-dual algorithm over a time-varying directed graph. With several classes of appropriately chosen decreasing parameter sequences and non-increasing event-triggered threshold sequences, we establish dynamic network regret and network cumulative constraint violation bounds. Finally, a numerical simulation example is provided to verify the theoretical results.

Keywords: Control over networks, Linear time-varying systems, Network analysis and control
Abstract: Temporality has been recently identified as a useful feature to exploit when controlling a complex network. Empirical evidence has in fact shown that, with respect to their static counterparts, temporal networks (i) are often endowed with larger controllable subspaces and (ii) require less control energy when steered towards an arbitrary target state. However, to date, we lack conditions guaranteeing that the dimension of the controllable subspace of a temporal network is larger than that of its static counterpart. In this work, we consider the case in which a static network is input connected but not controllable. We show that when the structure of the graph underlying the temporal network remains the same throughout each temporal snapshot while the edge weights vary (but stays different from 0), then the temporal network will be completely controllable almost always, even when its static counterpart is not. An upper bound on the number of snapshots needed to achieve controllability is also provided.

Keywords: Discrete event systems, Supervisory control, Automata
Abstract: In discrete event systems, it is often convenient and possible to observe directly whether or not the system is in a subset of the state space, typically after some delay, even if some event occurrences leading to the current state were not observed. We model supervisory control with the delayed observations of events and states and investigate the existence of a supervisor to obtain a given desired language accordingly. An existence verifier with polynomial run time is presented.

Keywords: Network analysis and control
Abstract: In this paper we analyze continuous-time SIS epidemics subject to arrivals and departures of agents, by using an approximated process based on replacements. In defining the SIS dynamics in an open network, we consider a stochastic setting in which arrivals and departures take place according to Poisson processes with similar rates, and the new value of the infection probability of an arriving agent is drawn from a continuous distribution. Since the system size changes with time, we define an approximated process, in which replacements take place instead of arrivals and departures, and we focus on the evolution of an aggregate measure of the level of infection. So long as the reproduction number is less than one, the long-term behavior of this function measures the impact of the changes of the set of agents in the epidemic. We derive upper bounds for the expectation and variance of this function and we include a numerical example to show that the approximated process is close to the original SIS process.

Keywords: Control over networks, Safety critical systems
Abstract: The paper focuses on the design of a control strategy for safety-critical remote teleoperation. The main goal is to make the controlled system track the desired velocity specified by an operator while avoiding obstacles despite communication delays. Control Barrier Functions (CBFs) are used to define the safety constraints that the system has to respect to avoid obstacles, while Model Predictive Control (MPC) provides the framework for adjusting the desired input, taking the constraints into account. The resulting input is sent to the remote system, where appropriate low-level velocity controllers translate it into system-specific commands. The main novelty of the paper is a method to make the CBFs robust against the uncertainties caused by the network delays affecting the system's state and do so in a less conservative manner. The results show how the proposed method successfully solves the safety-critical teleoperation problem, making the controlled systems avoid obstacles with different types of network delay. The controller has also been tested in simulation and on a real manipulator, demonstrating its general applicability when reliable low-level velocity controllers are available.

Keywords: Machine learning, Predictive control for nonlinear systems, Identification for control
Abstract: This paper presents an end-to-end learning approach to developing a Nonlinear Model Predictive Control (NMPC) policy, which does not require an explicit first-principles model and assumes that the system dynamics are either unknown or partially known. The paper proposes the use of available measurements to identify a nominal Recurrent Neural Network (RNN) model to capture the nonlinear dynamics, which includes constraints on the state variables and input. To address the issue of suboptimal control policies resulting from simply fitting the model to the data, the paper uses Reinforcement learning (RL) to tune the NMPC scheme and generate an optimal policy for the real system. The approach's novelty lies in the use of RL to overcome the limitations of the nominal RNN model and generate a more accurate control policy. The paper discusses the implementation aspects of initial state estimation for RNN models and integration of neural models in MPC. The presented method is demonstrated on a classic benchmark control problem: cascaded two tank system (CTS).

Keywords: Machine learning, Predictive control for nonlinear systems, Process control
Abstract: In this work, a negotiation agent trained by deep reinforcement learning methodology is employed in order to achieve control objectives in a multi-agent cooperative distributed implementation of model-based predictive control (MPC). The negotiation agent is located in the upper layer of a hierarchical control architecture to achieve a consensus between the different local MPC controllers, providing weighting coefficients for the negotiation process between the control sequences provided by the local controllers. The training algorithm for the reinforcement learning of this agent is the deep deterministic policy gradient algorithm (DDPG). The validation of the negotiation agent has been performed successfully both in simulation and in a real laboratory plant composed of eight coupled water tanks, which is a non-linear system characterized by high interaction between subsystems.

Keywords: Machine learning, Statistical learning, Cooperative control
Abstract: Consensus control in multi-agent systems has received significant attention and practical implementation across various domains. However, managing consensus control under unknown dynamics remains a significant challenge for control design due to system uncertainties and environmental disturbances. This paper presents a novel learning-based distributed control law, augmented by an auxiliary dynamics. Gaussian processes are harnessed to compensate for the unknown components of the multi-agent system. For continuous enhancement in predictive performance of Gaussian process model, a data efficient online learning strategy with a decentralized event-triggered mechanism is proposed. Furthermore, the control performance of the proposed approach is ensured via the Lyapunov theory, based on a probabilistic guarantee for prediction error bounds. To demonstrate the efficacy of the proposed learning-based controller, a comparative analysis is conducted,contrasting it with both conventional distributed control laws and offline learning methodologies.

Keywords: Machine learning, Neural networks, Aerospace
Abstract: This paper develops an intelligent flight controller for a fixed-wing aircraft model in the longitudinal plane, using a Reinforcement Learning(RL)-based control method, namely Deep Deterministic Policy Gradient(DDPG). The neural network controller is fed the values of aircraft position, velocity, pitch angle and pitch rate, and outputs the elevator deflection. Artificial Neural Network(ANN)s are used to approximate the nonlinear state-action value function and the policy function. Simulation results show that the trained flight controller learns from the experienced data how to fly over an obstacle wall with constrained pitch angle

Keywords: Machine learning, Optimization, Maritime
Abstract: In this paper, real-time weather and ship data will be used for mathematical modeling and cruise ship speed optimization. The ship data will be used for the construction of prediction models for hotel and auxiliary power consumption. Two different prediction model types will be compared: a simple polynomial model with linear parameters, as well as an artificial neural network. The effect of the ship’s speed will be predicted using voyage optimization software, which takes into account weather and sea forecasts as well as the ship’s hydrodynamic properties, for calculation of the required propulsion power as a function of speed. Total predicted power demand will be finally converted to fuel consumption, using information about the engine efficiencies. Furthermore, the associated cost will be attached to the edges of a graph, from which an optimal speed profile will be selected using dynamic programming. The performance of the models will be compared, and it is found that more than 3% of fuel savings are reported using both model types for the studied voyage.

Keywords: Intelligent systems
Abstract: The recent advances in space technology are focusing on fully autonomous, real-time, long-term orbit management and mission planning for large-scale satellite constellations in Low-Earth Orbit (LEO). Thus, a pioneering approach for autonomous orbital station-keeping has been introduced using a model-free Deep Policy Gradient-based Reinforcement Learning (DPGRL) strategy explicitly tailored for LEO. Addressing the critical need for more efficient and self-regulating orbit management in LEO satellite constellations, this work explores the potential synergy between Deep Reinforcement Learning (DRL) and Neuro-Evolution of Augmenting Topology (NEAT) to optimize station-keeping strategies with the primary goal to empower satellite to autonomously maintain their orbit in the presence of external perturbations within an allowable tolerance margin, thereby significantly reducing operational costs while maintaining precise and consistent station-keeping throughout their life cycle. The study specifically tailors DPGRL algorithms for LEO satellites, considering low-thrust constraints for maneuvers and integrating dense reward schemes and domain-based reward shaping techniques. By showcasing the adaptability and scalability of the combined NEAT and DRL framework in diverse operational scenarios, this approach holds immense promise for revolutionizing autonomous orbit management, paving the way for more efficient and adaptable satellite operations while incorporating the physical constraints of satellite, such as thruster limitations.

Keywords: Delay systems, Decentralized control, Stability of linear systems
Abstract: The decentralized strong stabilization problem, i.e., the problem of designing a decentralized stable stabilizing controller, is considered for linear time-invariant (LTI) multi-input multi-output time-delay systems. A characterization of decentralized blocking zeros is given and it is shown that the parity interlacing property, where decentralized blocking zeros, instead of centralized ones, should be used, is also a necessary condition for decentralized strong stabilizability of LTI time-delay systems. A numerical example is also presented to demonstrate a possible application of the theoretical results.

Keywords: Delay systems, Linear time-varying systems, Stability of linear systems
Abstract: Recently, a constructive approach to averagingbased stability was proposed for linear continuous-time systems with small parameter ε > 0 and rapidly-varying almost periodic coefficients. The present paper extends this approach to discrete-time linear systems with rapidly-varying periodic coefficients. We consider linear systems with state delays, where results on the stability via averaging are missing. Differently from the continuous-time, our linear matrix inequalities (LMIs) are feasible for any delay (i.e. the system is exponentially stable) provided ε is small enough. We introduce an efficient change of variables that leads to a perturbed averaged system, and employ Lyapunov analysis to derive LMIs for finding maximum values of the small parameter ε > 0 and delay that guarantee the exponential stability. Numerical example illustrates the effectiveness of the proposed approach.

Keywords: Delay systems, Stability of linear systems, Power electronics
Abstract: This paper presents a novel approach to develop a tuning rule for a Proportional-Integral-Retarded (PIR) controller when controlling a second-order system with unstable zeros. Particularly, we address the stability and performance issues that the right-half-plane zeros impose. With this aim, we use the multiplicity-induced-dominance properties to achieve a partial pole placement strategy guaranteeing the stability of the closed-loop system. Through this method, we derive analytical formulas for the parameters of the PIR controller that induce a predefined algebraic multiplicity for a group of real and dominant roots, ultimately enhancing system's response. Finally, numerical examples and a simulation benchmark conducted on a switched power converter show the effectiveness of the method.

Keywords: Stochastic control, Delay systems, Linear systems
Abstract: In this paper, we introduce a novel approach to solve the (mean-covariance) steering problem for a fairly general class of linear continuous-time stochastic systems subject to input delays. Specifically, we aim at steering delayed linear stochastic differential equations to a final desired random variable with given mean and covariance. We first establish a controllability result for these control systems, revealing the existence of a lower bound under which the covariance of the control system cannot be steered. This structural threshold covariance stems from a unique combined effect due to stochastic diffusions and delays. Next, we propose a numerically cheap approach to reach any neighbor of this threshold covariance in finite time. Via an optimal control-based strategy, we enhance the aforementioned approach to keep the system covariance small at will in the whole control horizon. Under some additional assumptions on the dynamics, we give theoretical guarantees on the efficiency of our method. Finally, numerical simulations are provided to ground our theoretical findings, showcasing the ability of our methods in optimally approaching the covariance threshold.

Keywords: Traffic control, Delay systems
Abstract: We construct a predictor-feedback cooperative adaptive cruise control (CACC) design with integral action, which achieves simultaneous compensation of long, actuation and communication delays, for platoons of heterogeneous vehicles whose dynamics are described by a third-order linear system with input delay. The key ingredients in our design are an underlying predictor-feedback law that achieves actuation delay compensation and an integral term of the difference between the delayed (by an amount equal to the respective communication delay) and current speed of the preceding vehicle. The latter, essentially, creates a virtual spacing variable, which can be regulated utilizing only delayed position and speed measurements from the preceding vehicle. We establish individual vehicle stability, string stability, and regulation for vehicular platoons, under the control design developed. The proofs rely on combining an input-output approach (in the frequency domain), with derivation of explicit solutions for the closed-loop systems, and they are enabled by the actuation and communication delays-compensating property of the design. We demonstrate numerically the control and model parameters’ conditions of string stability, while we also present simulation results, in a realistic scenario, considering a heterogeneous platoon of ten vehicles, for validating the performance of the design.

Keywords: Delay systems, Uncertain systems, Robust control
Abstract: This work proposes a closed-loop stability analysis of unstable open-loop systems represented by first-order and second-order plus time-delay (FOPTD and SOPTD) models. This analysis considers a proportional (P) controller and a predictor-based controller for FOPTD models, while a P and a phase-lead controller are considered for SOPTD models. In all cases, the achievable robustness has been computed considering time-varying delays. Several simulations were performed, and based on the integrated absolute error, it is shown that using a predictor is a better solution for large time-delay systems. Otherwise, the results are equivalent. In addition, the effect of the left half-plane pole of the SOPTD system is analysed. Finally, a comparison is made with recently published results that employ more complex and iterative algorithms.

Keywords: Quantized systems, Sampled data control, Stability of linear systems
Abstract: This study's topic is stabilizing a linear time-invariant plant subjected to bounded disturbance using quantized state feedback. We propose an adaptive quantization scheme consisting of two terms, a natural decay term, and a positive coupling proportional to the norm of the state. The proposed procedure ensures the utilization of a lower quantizer resolution if the system is steered away from the origin due to disturbances. In conjunction with dynamic quantization, the event-triggered approach is motivated by the need to utilize computing resources efficiently. The proposed control law and dynamic quantization ensure the system is input-to-state stable (ISS). We ensure that Zeno's phenomenon is absent by showing that the inter-event times are lower bound. We compare the average number of bits the controller requires between the proposed reactive control law and a controller with constant quantization error on a numerical example.

Keywords: Fault diagnosis, Linear time-varying systems, Stability of linear systems
Abstract: This paper introduces a novel approach, which we refer to as hybrid moving controller, designed to ensure closed-loop stability while eliminating the requirement for synchronization between the plant and control unit. In our proposed method, the controller is time-varying and moves the closed-loop eigenvalues along radial trajectories originating from the origin. The sequence of controllers is assumed to be kept confidential from potential adversaries. Given that this moving controller renders the overall closed-loop system time-varying, maintaining the eigenvalues within the unit circle alone is insufficient to guarantee stability. As a result, we explore stability through the lens of contraction theory and present criteria for the sequence of controllers to ensure stability.

Keywords: Nano systems, Linear systems, Servo control
Abstract: The aim of this paper is to study the applicability of the control allocation (CA) framework for dualstage actuator (DSA) systems - a subclass of overactuated precise positioning devices, characterised by serial connection of a coarse and fine stage. In contrast to the typical control frameworks developed for DSAs, such as master-slave and decoupled design, the CA framework approaches the control in a modular way, clearly separating the motion control from the allocation of efforts among the actuators. To achieve this, the concept of redundant controllability is introduced with the aim to analyse weakly input redundant LTI systems to determine the virtual effort that enables the separation of the system in terms of the CA approach. As a result, it shows that with serially connected actuators, the redundancy is clearly present in the displacement domain. In addition, the application of the framework is shown and the possible control approaches are discussed.

Keywords: Maritime, Emerging control applications, Autonomous systems
Abstract: Future aquaculture operations demand a higher degree of autonomy as new aquaculture locations are established in more exposed environments. In this study, we evaluate the feasibility of automating traditional fish farm operations through the utilization of a vessel-mounted robotic arm. We focus on a novel approach for automating the removal of deceased fish. Our work encompasses the technical design of a hose attachment and hook mechanism, simulations replicating realistic vessel and net pen motions, real-time prediction of fish cage collar positions using autoregressive models, and motion compensation control for the robotic arm. Scaled experiments indicate the feasibility of the proposed concept from a control perspective. This research contributes to the broader research challenge in robotics of managing interactions between a robotic arm mounted on a mobile platform and an environment that is also in motion.

Keywords: Emerging control applications, Mechatronics
Abstract: This research examines the issue of state-constrained stabilizing controllers for Bouc-Wen hysteresis control systems with all hysteresis parameters being unknown. We develop a novel hysteresis estimator for estimating the virtual hysteresis state. Employing the L_2-gain control approach effectively mitigates the impact of estimation errors on the system. With barrier functions, even when the hysteresis parameters are unknown, we can formulate state-constrained stabilizing controllers using solutions to linear matrix inequalities. Ultimately, we showcase the efficacy and practicality of this control strategy with the help of a numerical example.

Keywords: Hybrid systems, Mechatronics
Abstract: We present a frequency-domain stability analysis tool for the feedback interconnection of a linear time-invariant multi-input-multi-output plant and a nonlinear multi-input multi-output controller. The controller consists of several hybrid elements interconnected with well-crafted linear filters that together enable favourable phase properties and allow for improved controller performance. Stability and performance of the proposed controller design is evaluated on a wafer stage of an industrial metrology inspection machine.

Keywords: Observers for nonlinear systems, Biological systems, Optimization
Abstract: Dynamic operation of biological processes, such as anaerobic digestion (AD), requires reliable process monitoring to guarantee stable operating conditions at all times. Unscented Kalman filters (UKF) are an established tool for nonlinear state estimation, and there exist numerous variants of UKF implementations, treating state constraints, improvements of numerical performance and different noise cases. So far, however, a unified comparison of proposed methods emphasizing the algorithmic details is lacking. The present study thus examines multiple unconstrained and constrained UKF variants, addresses aspects crucial for direct implementation and applies them to a simplified AD model. The constrained UKF considering additive noise delivered the most accurate state estimations. The long run time of the underlying optimization could be vastly reduced through pre-calculated gradients and Hessian of the associated cost function, as well as by reformulation of the cost function as a quadratic program. However, unconstrained UKF variants showed lower run times at competitive estimation accuracy. This study provides useful advice to practitioners working with nonlinear Kalman filters by paying close attention to algorithmic details and modifications crucial for successful implementation.

Keywords: Observers for nonlinear systems, LMI's/BMI's/SOS's, UAV's
Abstract: This paper deals with the observer design for continuous-time nonlinear systems with external disturbances. It suggests a new observer structure that relies on an output-based dynamic extension strategy enabling the state observer to be less affected by measurement noise. The proposed observer is based on new Linear Matrix Inequality (LMI) condition guaranteeing the Input-to-State Stability (ISS) property of the estimation error.

Keywords: Observers for nonlinear systems, Stability of nonlinear systems, Biological systems
Abstract: This paper proposes a state estimator for a class of nonlinear systems that includes the Persidskii systems with bilinear cross-terms. The estimation error analysis is based on the input-to-output stability theory and formulated using linear matrix inequalities. Simulations are provided for a model of consumer-resource interaction.

Keywords: Observers for nonlinear systems, Delay systems, LMI's/BMI's/SOS's
Abstract: This paper deals with nonlinear observer design for systems with delayed nonlinear outputs. The main idea behind this work consists of using a dynamic extension technique to transform a system with delayed nonlinear outputs into a system with linear outputs and a delay-dependent integral term in the dynamic process. First, a general result for arbitrary nonlinear structures is proposed, and then further contributions are provided for a specific family of systems, namely systems in companion form for which we obtain novel high-gain observer synthesis conditions.

Keywords: Observers for nonlinear systems, Hybrid systems, Aerospace
Abstract: This paper presents an approach for the design of globally asymptotically stable pose and velocity-bias observers. Based on the generic framework for pose and velocity-bias observers, we construct synergistic potential functions on SE(3) and SO(3) via angular warping and use the gradients of the formers to construct innovation terms. Under the synergistic-based hybrid method, different potential functions are selected along with the innovation terms to avoid the undesired critical points on the manifold. The proposed observer can be expressed in terms of measurements and modified measurements in some cases. Simulations are also conducted to show the advantages of the synergistic-based hybrid observer over the reset-based hybrid observer.

Keywords: Observers for nonlinear systems, Predictive control for nonlinear systems, Identification for control
Abstract: This paper presents the implementation of a Moving Horizon Estimation (MHE) approach for the simultaneous estimation of state and parameters within Pressurized Water Reactors (PWR) used in Nuclear Power Plants (NPPs). Addressing the inherent model stiffness, we leverage collocation as the integration method, making direct collocation a natural choice for transcription of the optimization problem into a nonlinear programming problem (NLP). The implementation benefits from state-of-the-art tools for modeling, expressing, and solving optimization problems, specifically CasADI and IPOPT. In a comparative analysis with a standard Extended Kalman Filter (EKF), our proposed MHE method exhibits superior performance and accuracy, even with rather classical tuning parameters.

Keywords: Power plants, Optimization, Mechatronics
Abstract: Skysails Power GmbH is the leading manufacturer of light and efficient power kites that harness the wind’s untapped supplies at high altitudes, aiming at profoundly altering wind energy’s impact in achieving the global energy transition. Novel, variable trim kites have been developed that allow to modulate the aerodynamic coefficients of the airborne system, significantly improving the overall system efficiency.

The flight control of variable trim kites is much more complex than that of previous kite generations and its mastering is a challenge and one of the keys to a successful operation. Numerical optimization is applied to find a set of flight trajectories in order to maximize the energy production while satisfying several constraints on the system operating in a wide range of conditions.

This industry abstract provides a general introduction of the trajectory optimization problem with variable trim kites. We also briefly introduce the state-of-the-art optimization setup.  This is followed by demonstration of high-quality example results of the optimization. Finally, we discuss the results and their applications.

Keywords: Optimal control, Power plants, Aerospace
Abstract: Some aspects of the approach at EnerKíte to design an optimize future systems are discussed. A steady-state airborne wind energy system model is presented, and a path and system design optimization process is explained. The path and control optimization process looks for the best trajectory and control inputs for a given plant, while the plant optimization process seeks the smallest plant that results in our nominal total output at our desired wind-speed and operating altitude. Small plant here means a linear combination of peak motor power, wing area and maximum force, which are the main cost drivers or airborne wind energy systems.

Keywords: Machine learning, Sensor and signal fusion, Neural networks
Abstract: Indoor localization systems play a pivotal role in various applications, ranging from asset tracking in industrial settings to personalized navigation assistance in shopping malls. This paper presents a novel real-time indoor localization system designed for quick and easy deployment. The core positioning technology is based on self-supervised learning, which allows people to collect unlabelled data by themselves and then to build own localization system. The algorithm utilizes WiFi signal strength and inertial sensors embedded within smartphones to continuously estimate the user's position in real-time. A web-platform basically shows a thousand of current and past locations of users whose smartphone connected to the server. Also, it has geofencing that alarms users who go across boarder of some designated area such as dangerous zone. Proof of concept has been done from many industrial factories, warehouses, subway stations, offices and malls.

Keywords: Robotics, Uncertain systems
Abstract: 5G networks enable mobile robots to offload computationally expensive processes to external processing units, such as edge computers, which can lower the hardware requirements for the robot. One application the robot can benefit from offloading is sensor-based localization, since processing of sensor data usually is expensive. However, if multiple robots transmit sensor data over the network, this could introduce new problems, since the network has a limited capacity. At the same time, not transmitting the sensor data will affect the quality of the localization estimates. Based on previous work on uncertainty-aware safe navigation, we focus on localization uncertainty to measure the quality. We investigate how the uncertainty is affected by limiting the bandwidth for sensor data, and then propose a framework that can allocate bandwidth necessary for a desired level of localization uncertainty.

Keywords: Adaptive control, Transportation systems
Abstract: To absorb the inevitable so called second harmonic input power component of a propulsion system for a single phase ac fed electric train, large and bulky hardware filters are traditionally added, These dedicated hardware filters keep the intermediate so called dc-link voltage smooth, but add cost, weight and space to the system. Removing the filters instead leads to large second harmonic dc-link voltage oscillations, which may propagate to corresponding motor torque oscillations. The oscillating dc-link voltage component is sinusoidal, where the frequency is twice the supply frequency and the amplitude is proportional to the system power. Even with traditional broadband suppression of dc-link voltage variations, the remaining second harmonic torque ripple may still be large enough to damage mechanical components of the system, and to cause annoying noise in the car body. To enable practical operation without second harmonic hardware filters, improved disturbance suppression of second harmonic torque oscillations is therefore required. To handle a wide range of operating conditions, an generalized adaptive feedforward cancellation (AFC) scheme is applied, based on the measured dc-link voltage. Although realizing a linear time-varying control law, the applied suppression algorithm with can in fact be represented by a rational transfer function. This result is used in this contribution to appropriately shape the control law to achieve perfect steady-state disturbance suppression, while still keeping additional control loops of the system stable.

Keywords: Aerospace, Optimal control, Sensor and signal fusion
Abstract: This paper aims to describe and provide a preliminary assessment of Guidance, Navigation, and Control (GNC) technologies for a re-entry system with an Inflatable Heat Shield (IHS), within the scope of the EFESTO2 project funded by the European Commission (EC). The work performed is focused on the development of i) a novel closed-loop guidance approach, ii) a hybrid navigation subsystem based on a Consider Extended Kalman Filter (CEKF), and iii) a controller designed accounting for robustness and performance goals. The objective of the GNC is set to initial re-entry dispersion of the vehicle, to allow for an easier and less costly a Mid-Air Retrieval (MAR).

Keywords: Transportation systems
Abstract: Since a few years ago there is a trend towards higher levels of autonomy in many industries. Depending on type of industries there are different business motivations for this. It may be safety by removing personell from dangeorous work environments, or by reducing the risk of human mistakes. Alternative drivers include projected future lack of personell, or more traditional automation drivers such as increased productivity or improved product quality.

This panel will specifically focus on autonomy in transportation, sometimes as one part of a larger industrial complex. There will be presentations on autonomous vehicles in mining, both under ground and above ground, autonomous driving, both on-road and off-road, as well as autonomous aircraft. Following initial presentations by the 5 panelists there will be a moderated discussion on common challenges among these diverse areas of autonomous transportation, as well as potential differences between these different applications.

Panelists: Kristin Nielsen, Epiroc , Sweden, Pascal Laurens, Airbus, France, Johan Sjöberg, Volvo, Sweden, Erik Möllerstedt, Aurora, USA, Kalevi Tervo, ABB, Finland

Keywords: Stochastic control
Abstract: Thermodynamics was born in the 19th century in quest of a way to quantify efficiency of steam engines at the dawn of the industrial age. In the time since, thermodynamics has impacted virtually all other areas in science, from chemistry and biology to the physics of black holes, and yet, progress beyond the classical quasi-static limit towards finite-time thermodynamic transitions has been slow; finite-time is of essence for non-vanishing power generation. In recent years a deeper understanding of non-equilibrium processes has been achieved based on stochastic models with degrees of freedom (state variables) that are subject to Brownian excitation that models heat baths. Within this framework we will explain energy transduction, we will give insights on how anisotropy in thermal or chemical potentials can be tapped for power generation in engineered and physical processes, and we will highlight fundamental bounds on the amount of power that can drawn during finite-time thermodynamic transitions.

The talk is based on joint works with Rui Fu, Olga Movilla, Amir Taghvaei and Yongxin Chen. Research funding by AFOSR, ARO and NSF is gratefully acknowledged.

Keywords: Electrical power systems
Abstract: The push towards replacing fossil-fuel and nuclear based power plants with renewable generation and the electrification of the transport and the heating sector overall result in less predictable electric energy supply and a significant increase in the demand. Furthermore, the fact that wind and PV resources are connected to the power system via inverters changes the system dynamics. Despite all these challenges, there are also new opportunities such as the higher degree of freedom in terms of control of inverter-connected systems or the fact that the additional load is flexible. Leveraging the availability of large amounts of data and the deployment of ubiquitous automation play crucial roles in addressing the challenges and seizing the opportunities in future electric power systems. The goal of this talk is to show examples of how data and control can support the transition to sustainable energy system which is becoming increasingly complex to operate.

Keywords: Control education
Abstract: During the 22nd European Control Conference, to be held in Stockholm during 25-28 June 2024, a special session Women in Control will take place on Thursday, June 27, following the successful Women in Control event at the 21st European Control Conference, held in Bucharest, Romania, in 2023. The Women in Control lunch event aims to provide a networking opportunity for women. Its focus is to offer new perspectives on the current role of engineering, and particularly control engineering, along with discussions on programs, opportunities, and personal stories from women, in both academia and industry.

Panelists/speakers: Maria Elena Valcher (University of Padova) - intro Laura Dal Col (King) - pending Steffi Knorn (Technische Universität Berlin)

Keywords: Process control
Abstract: Research centres are vital players in the international research landscape. They provide a platform where academia, industry and the public sector can jointly develop solutions to pressing societal problems. Europe is home to several such centres with emphasis on control, automation, digitalisation, or AI, both in industry and in academia. Though research excellence is of course at the heart of their activities, it is often only the beginning of their story. Their size, resources, and the diversity of their members allows research centres to develop a holistic approach, expanding beyond research to technology transfer, equal opportunities, outreach, and education at all levels. This luncheon will provide a 360^o-view of how through these centres industry, academia, and agencies are joining forces to reach the critical mass needed to tackle global challenges. The discussion will provide insights into topics such as how such centres get founded, how their activities develop and diversify over time, the role of industry and funding agencies, and how everyone can get involved!

Panelists/speakers: Magnus Frodigh (Ericsson Research) – pending Gabriela Hug (ETH Zurich, NCCR Automation) Karl H. Johansson (KTH, Digital Futures) Sara Mazur (Knut and Alice Wallenberg Foundation) – pending Marios Polycarpou (University of Cyprus, KIOS) Munther Dahleh (MIT)

Keywords: Adaptive control, Linear systems
Abstract: Funnel control achieves output tracking with guaranteed tracking performance for unknown systems and arbitrary reference signals. In particular, the tracking error is guaranteed to satisfy time-varying error bounds for all times (it evolves in the funnel). However, convergence to zero cannot be guaranteed, but the error often stays close to the funnel boundary, inducing a comparatively large feedback gain. This has several disadvantages (e.g. poor tracking performance and sensitivity to noise due to the underlying high-gain feedback principle). In this paper, therefore, the usually known reference signal is taken into account during funnel controller design, i.e. we propose to combine the well-known internal model principle with funnel control. We focus on linear systems with linear reference internal models and show that under mild adjustments of funnel control, we can achieve asymptotic tracking for a whole class of linear systems (i.e. without relying on the knowledge of system parameters).

Keywords: Adaptive control
Abstract: The paper explores in detail the use of dynamic adaptation gain/step size (DAG) for improving the adaptation transient performance of variable step-size LMS (VS-LMS) adaptation algorithms. A generic form for the implementation of the DAG within the VS-LMS algorithms is provided. Criteria for the selection of the coefficients of the DAG filter which is required to be a strictly positive real transfer operator are given. The potential of the VS-LMS adaptation algorithms using a DAG is then illustrated by experimental results obtained on a relevant adaptive active noise attenuation system.

Keywords: Adaptive control, Distributed control, Large-scale systems
Abstract: Control of network systems with uncertain local dynamics has remained an open problem for a long time. In this paper, a distributed minimax adaptive control algorithm is proposed for such networks whose local dynamics has an uncertain parameter possibly taking finite number of values. To hedge against this uncertainty, each node in the network collects the historical data of its neighboring nodes to decide its control action along its edges by finding the parameter that best describes the observed disturbance trajectory. Our proposed distributed adaptive controller is scalable and we give both lower and upper bounds for its ell_{2} gain. Numerical simulations demonstrate that once each node has sufficiently estimated its local uncertainty, the distributed minimax adaptive controller behaves like the optimal distributed mathcal{H}_{infty} controller in hindsight.

Keywords: Adaptive control, Statistical learning, Output feedback
Abstract: In this paper, we analyze the regret incurred by a computationally efficient exploration strategy, known as naive exploration, for controlling unknown partially observable systems within the Linear Quadratic Gaussian (LQG) framework. We introduce a two-phase control algorithm called LQG-NAIVE, which involves an initial phase of injecting Gaussian input signals to obtain a system model, followed by a second phase of an interplay between naive exploration and control in an episodic fashion. We show that LQG-NAIVE achieves the optimal regret growth rate up to logarithmic factors, and we validate its performance through numerical simulations. Additionally, we propose LQG-IF2E, which extends the exploration signal to a `closed-loop' setting by incorporating the Fisher Information Matrix (FIM). We provide compelling numerical evidence of the competitive performance of LQG-IF2E compared to LQG-NAIVE.

Keywords: Robotics, Adaptive control, Robust adaptive control
Abstract: This paper deals with robust-adaptive control of Parallel Kinematic Manipulators (PKMs), where a novel super-twisting L1 adaptive controller is proposed. The objective is to increase the robustness towards uncertainties as well as external disturbances of the standard L1 adaptive controller, by incorporating a robust super-twisting term. The proposed controller as well as the original L1 adaptive controller, are detailed for robot manipulators. Next, the experimental testbed is described, along with some implementation issues on FOEHN parallel robot. The proposed control scheme is compared with some existing literature controllers in two experimental scenarios, highlighting notable improvements in tracking performance reaching up to 75% with respect to the standard L1 adaptive controller.

Keywords: Robust adaptive control, Mechatronics, Adaptive control
Abstract: This article provides an experimental case study of a state-of-the-art recursive feedforward parameter learning framework on a high-tech industrial metrology and inspection machine. The aim of the learning framework is to recursively adapt the feedforward parameters to compensate for time-varying and position-dependent system behavior, e.g., caused by wear, position and temperature dependent actuator characteristics, changes in shape and stiffness due to thermal expansion, and sample time jitter. The strength of the approach is demonstrated through experiments on a high-tech motion system which show a peak error reduction of circa 45% compared to the industrial controller with offline calibrated feedforward parameters.

Keywords: Predictive control for nonlinear systems, Autonomous robots, Optimization algorithms
Abstract: NMPC is a popular strategy for solving motion planning problems appearing in autonomous driving applications that include collision avoidance constraints. Non-smooth obstacle shapes, such as rectangles, introduce additional local minima in the underlying optimization problem. Using smooth over-approximations, e.g., ellipsoidal shapes, limits the performance due to their conservativeness. We propose to vary the smoothness and the related over-approximation by a homotopy. Instead of varying the smoothness in consecutive sequential quadratic programming iterations, we use formulations that decrease the smooth over-approximation from the end towards the beginning of the prediction horizon. Thus, the real-time iteration scheme applies to the proposed NMPC formulation, i.e., only one quadratic program needs to be solved at each time step. Different formulations are compared in simulation experiments and shown to successfully improve performance without increasing the computational burden.

Keywords: Predictive control for nonlinear systems, Constrained control, Computational methods
Abstract: One of the fundamental issues in Model Predictive Control (MPC) is to be able to guarantee the recursive feasibility of the underlying receding horizon optimization. In other terms, the primary condition for a NMPC design is to ensure the closed-loop solution remains indefinitely within a safe set. This issue can be addressed by introducing a terminal constraint described in terms of a control invariant set. However, the control invariant sets of nonlinear systems are often impractical to use due to their complexity. The K-step control invariant sets are representing generalizations of the classical one-step control invariant sets and prove to retain the useful properties for MPC design, but often with simpler representations, and thus greater applicability. In this paper, a novel NMPC scheme based on K-step control invariant sets is proposed. We employ symbolic control techniques to compute a K-step control invariant set and build the NMPC framework by integrating this set as a terminal constraint, thereby ensuring recursive feasibility.

Keywords: Predictive control for nonlinear systems, Optimal control, Constrained control
Abstract: Economic model predictive control (EMPC) is a popular control methodology that enjoys attention both from practitioners as well as the control research community. Of particular interest are EMPC schemes without terminal constraints in the underlying optimal control problems, and a considerable amount of theoretical analyses are already available. In this work, we derive many of these results using the notion of local incremental stabilizability, a concept that proved to be important in robust model predictive control. We show that this notion can be seamlessly used in the analysis of EMPC, and also derive new continuity results, replacing a corresponding assumption in existing works.

Keywords: Predictive control for nonlinear systems, Optimal control
Abstract: In this work, a predictive controller that uses online-learned terminal costs is proposed. The learned costs are based on Approximate Dynamic Programming (ADP), specifically, Value Iteration (VI), an ADP technique that aims to iteratively determine the optimal value function of an optimal control problem. With this, we aim to improve infinite-horizon controller performance. Instead of performing an offline iteration over the whole state space, we consider a local update law which is executed online and reduces the computational burden. We first extend results on local VI to the case where the iteration is initialized with the value function of a stabilizing feedback policy, showing that the local update law preserves the stability of the associated control law. Then, we use the approximated cost function in a predictive controller framework and provide recursive feasibility, stability guarantees and an estimate of the region of attraction for a sufficiently long prediction horizon. The proposed approach is evaluated in simulation against a predictive controller which uses VI over the whole state space, and a predictive controller without terminal costs, to show the advantages of the proposed controller.

Keywords: Predictive control for nonlinear systems, Optimization, Neural networks
Abstract: Model predictive control (MPC) is a powerful control method for handling complex nonlinear systems that are subject to constraints. However, the real-time application of this approach can be severely limited by the need to solve constrained nonlinear optimization problems at each sampling time. To this end, this work introduces a novel learning-based iterative solver that provides highly accurate predictions, optimality certification, and fast evaluation of the MPC solution at each sampling time. To learn this iterative solver, we propose an unsupervised training algorithm that builds on the Karush-Kuhn-Tucker optimality conditions, modified by a Fischer-Burmeister formulation, and eliminates the need for prior sampling of exact optimizer solutions. By exploiting efficient vector-Jacobian and Jacobian-vector products via automatic differentiation, the proposed training algorithm can be efficiently executed. We demonstrate the potential of the proposed learning-based iterative solver on the example of nonlinear model predictive control of a nonlinear double integrator. We show its advantages when compared to exact optimizer solutions and with an imitation learning-based approach that directly obtains a data-based approximation of the MPC control law.

Keywords: Electrical machine control, Observers for nonlinear systems, Automotive
Abstract: The current developments in electric drive systems are directed towards high rotational speeds and partial operation in the (thermal) overload range. This makes nonlinear control theory increasingly common as the demands of high-performance drives increase, and high computing power becomes more accessible and available. However, high-speed drives are also confronted with a low sample-to-fundamental frequency ratio, which is particularly challenging for the observer design. In this case, the accurate timing of the measurements within the pulse-width modulation (PWM) pattern becomes relevant but is mostly neglected in the observer design. Therefore, this work investigates essential aspects of developing nonlinear state and parameter observers (extended Kalman filter) for induction machines, especially considering the timing of the PWM and current measurements. An observer is proposed, which systematically takes into account these effects. Simulation and measurement results prove that the proposed observer significantly improves estimation accuracy compared to the state of the art when electric drives with a low sample-to-fundamental frequency are considered.

Keywords: Transportation systems, Traffic control, Optimization algorithms
Abstract: This work proposes a novel solution approach to address the On-Time Arrival (OTA) problem, considering macroscopic traffic dynamics. The OTA problem is formulated as a nonconvex, nonlinear, multi-objective optimization problem considering two objective criteria. The first criterion aims at minimizing the travel time of all drivers in the network to prevent congestion, while the second criterion seeks to minimize the discrepancy between the desired and actual arrival time. The proposed formulation is solved efficiently through an approximated convex solution that leverages the Normal Boundary Intersection (NBI) method to efficiently generate a representative sample of the Pareto Front. Additionally, a solution methodology based on the Nash Bargaining Game is proposed to select a unique solution across all the Pareto points. Finally, simulation results demonstrate that the proposed solution can eliminate congestion while ensuring that most drivers will arrive at their destination on their desired time.

Keywords: Automotive, Energy systems, Optimal control
Abstract: The increasing interest in hybridization and electrification of racing cars is pushing towards the design of dedicated energy storage systems. Among them, Hybrid Battery Packs (HBPs) represent an interesting solution, especially in racing, due to the joint presence of high-power and high-energy requirements, needed to guarantee the desired race mileage while maximizing performance. To realize a HBP, a real-time control law, named Energy Management Strategy (EMS), is pivotal to properly split the power while satisfying the driver's request. In this paper, we investigate whether the control laws that emerged for traditional vehicles can be employed in the racing scenario. Considering a Formula E case study, the well-known Equivalent Consumption Minimization Strategy (ECMS) and a classical filter-based approach are compared to the race time-optimal implicit power distribution. Analyses firstly evaluate the capability of each EMS in matching the implicit solution, showing the superior performance of ECMS. Then, explicitly including each real-time EMS in the time-optimal problem, the race times are re-optimized to evaluate the actual loss of performance. Finally, we highlight how the combination of each EMS with a dedicated battery sizing strategy can influence the overall performance, closing the gap among the different power split solutions.

Keywords: Traffic control, Transportation systems
Abstract: The integration of Automated Vehicles (AVs) into the traffic flow holds the potential to significantly improve traffic congestion by enabling AVs to function as actuators within the flow. This paper introduces an adaptive speed controller tailored for scenarios of mixed autonomy, where AVs interact with human-driven vehicles. We model the traffic dynamics using a system of strongly coupled Partial and Ordinary Differential Equations (PDE-ODE), with the PDE capturing the general flow of human-driven traffic and the ODE characterizing the trajectory of the AVs. A speed policy for AVs is derived using a Reinforcement Learning (RL) algorithm structured within an Actor-Critic (AC) framework. This algorithm interacts with the PDE-ODE model to optimize the AV control policy. Numerical simulations are presented to demonstrate the controller impact on traffic patterns, showing the potential of AVs to improve traffic flow and reduce congestion.

Keywords: Traffic control, Transportation systems, Hybrid systems
Abstract: We study the Traffic Light Control (TLC) problem for a single intersection, considering both straight driving vehicle flows and corresponding crossing pedestrian flows with the goal of achieving a fair jointly optimal sharing policy in terms of average waiting times. Using a stochastic hybrid system model, we design a quasi-dynamic policy controlling the traffic light cycles with several threshold parameters applied to the light cycles and the partially observed contents of vehicle and pedestrian queues. Infinitesimal Perturbation Analysis (IPA) is then used to derive a data-driven gradient estimator of a cost metric with respect to the policy parameters and to iteratively adjust these parameters through an online gradient-based algorithm in order to improve overall performance on this intersection and adapt the policy to changing traffic conditions. The controller is applied to a simulated intersection in the town of Veberöd, Sweden, to illustrate the performance of this approach using real traffic data from this intersection.

Keywords: Traffic control, Transportation systems, Intelligent systems
Abstract: The coordination of signalized intersections in urban cities improves both traffic operations and environmental aspects. Traffic signal coordination has a long history, where the impact of offset on delays and emissions at signalized intersections have been investigated through simulations and a limited number of experimental findings. Coordinating intersections is often warranted by specific engineering requirements and judgment. However, as a consequence, many intersections in cities remain without coordination. In this paper, we examine the potential benefits of coordi- nating signalized intersections at scale. Unlike previous studies, our analysis is based on aggregated anonymized probe data analysis and does not need to explicitly model traffic-oriented issues such as queue spillback and platoon dispersion. We follow a quantitative approach by considering an intersection pair, i.e. a system of two signalized intersections which can be spatially coupled. We introduce a new method for coordinating those signalized intersections. The method first evaluates the effect of different offsets on vehicle travel times and fuel consumption (or emissions). Then, it coordinates the two intersections by setting a common cycle and finding the optimal offset that minimizes fuel consumption and/or travel times. We present the analysis for several case studies from real intersections at Jakarta, Rio de Janeiro, Kolkata, and Haifa. Finally, we evaluated our method by implementing it in a real experimental study at Jakarta. We collaborated with the city to implement the optimal offset determined by the proposed method, and we compared the results before and after coordination.

Keywords: Automotive, Transportation systems, Emerging control applications
Abstract: This paper introduces an automatic control method designed to enhance the operation of electric vehicles, besides the speed tracking objectives, by including reliability and lifetime requirements. The research considers an automotive power converter which supplies electric power to a permanent magnet synchronous motor (PMSM). The primary control objective is to mitigate the thermal stress on the power electronic Insulate Gate Bipolar Transistors (IGBTs), while simultaneously ensuring effective speed tracking performance. To achieve these goals, we propose an extended mathcal{H}_infty design framework, which includes reliability models. The method is tested in two distinct scenarios: reliability-aware, and reliability-free cases. Furthermore, the paper conducts a lifetime analysis of the IGBTs, leveraging the Rainflow algorithm and temperature data.

Keywords: Agents and autonomous systems, Cooperative control, Lyapunov methods
Abstract: This paper presents a framework designed to address the challenges of runtime formation switching and collision avoidance for multi-agent systems (MASs) operating under undirected and switching network topologies. Within this framework, we investigate time-varying formation tracking (TVFT) control for linear multi-agent systems in the presence of actuator failures and maneuvering leader. One of the main challenges in designing a formation tracking controller is the leader’s independent selection of diverse time-varying formation (TVF) configurations for the followers without pre-specifying them, with each configuration lasting for specific durations. To address this challenge, we introduce a set of distributed observers, each concurrently estimating these formation configurations for its assigned follower. Using these estimates and the collision-avoidance algorithm, the proposed controller enables the followers to synchronize with the leader’s chosen runtime formations, all without any collisions. Moreover, uniform ultimate boundedness (UUB) of followers' collective formation tracking error is rigorously guaranteed using Lyapunov’s stability theory. Finally, a simulation example illustrating the results is given.

Keywords: Agents and autonomous systems, Decentralized control, Autonomous robots
Abstract: In this paper, we present a novel 3D formation control scheme for directed graphs in a leader-follower configuration, achieving (almost) global convergence to the desired shape. Specifically, we introduce three controlled variables representing bispherical coordinates that uniquely describe the formation in 3D. Acyclic triangulated directed graphs (a class of minimally acyclic persistent graphs) are used to model the inter-agent sensing topology, while the agents' dynamics are governed by single-integrator model. Our analysis demonstrates that the proposed decentralized formation controller ensures (almost) global asymptotic stability while avoiding potential shape ambiguities in the final formation. Furthermore, the control laws are implementable in arbitrarily oriented local coordinate frames of follower agents using only low-cost onboard vision sensors, making it suitable for practical applications. Finally, we validate our formation control approach by a simulation study.

Keywords: Agents and autonomous systems, Nonlinear system theory, Robotics
Abstract: We propose a threshold decision-making framework for controlling the physical dynamics of an agent switching between two spatial tasks. Our framework couples a nonlinear opinion dynamics model that represents the evolution of an agent's preference for a particular task with the physical dynamics of the agent. We prove the bifurcation that governs the behavior of the coupled dynamics. We show by means of the bifurcation behavior how the coupled dynamics are adaptive to the physical constraints of the agent. We also show how the bifurcation can be modulated to allow the agent to switch tasks based on thresholds adaptive to environmental conditions. We illustrate the benefits of the approach through a multi-robot task allocation application for trash collection.

Keywords: Agents and autonomous systems, Safety critical systems, Robotics
Abstract: In this article, we propose a dynamical system to avoid obstacles which are star shaped and simultaneously converge to a goal. The convergence is almost-global in a domain and the stationary points are identified explicitly. Our approach is based on the idea that an ideal vector field which avoids the obstacle traverses its boundary up to when a clear path to the goal is available. We show the existence of this clear path through a set connecting the boundary of the obstacle and the goal. The traversing vector field is determined for an arbitrary obstacle (described by a set of points) by separating it into cluster of stars. We propose an algorithm which is linear in number of points inside the obstacle. We verify the theoretical results presented with various hand drawn obstacle sets. Our methodology is also extended to obstacles which are not star-shaped, and, those which exist in high dimensions.

Keywords: Agents and autonomous systems, Maritime
Abstract: Autonomous multi-agent systems are increasingly being deployed in environments where winds and ocean currents have a significant influence. Recent work has developed control policies for single agents that leverage flows to achieve their objectives in dynamic environments. However, in multi-agent systems, these flows can cause agents to collide or drift apart and lose direct inter-agent communications, especially when agents have low propulsion capabilities. To address these challenges, we propose a hierarchical multi-agent control approach that allows arbitrary single-agent performance policies that are unaware of other agents to be used in multi-agent systems while ensuring safe operation. We first develop a safety controller using potential functions, solely dedicated to avoiding collisions and maintaining inter-agent communication. Next, we design a low-interference safe interaction (LISIC) policy that trades off the performance policy and the safety control to ensure safe and performant operation. Specifically, when the agents are at an appropriate distance, LISIC prioritizes the performance policy while smoothly increasing the safety controller when necessary. We prove that under mild assumptions on the flows experienced by the agents, our approach can guarantee safety. Additionally, we demonstrate the effectiveness of our method in realistic settings through an extensive empirical analysis with simulations of fleets of underactuated autonomous surface vehicles operating in dynamic ocean currents where these assumptions do not always hold.

Keywords: Agents and autonomous systems, Autonomous robots, Agents networks
Abstract: This paper studies a rigid graph theory and distance geometry based approach to the autonomous vehicle station-keeping problem for situations where global positioning is not available. We propose a control scheme for the station keeping of an autonomous vehicle A, i.e., the task of navigating A to a target point T whose distances are predefined from a set of beacons, where A is not capable of measuring its self-position. The beacons can be stations or other autonomous vehicles, and A is assumed to have nonholonomic unicycle motion kinematics and can measure only distances to the beacons. In the proposed control scheme, the vehicle-beacon range measurements are mapped to the estimate of the distance to T utilizing notions of globally rigid graphs for guaranteeing unique estimation, short-term odometry, and triangulation techniques. The overall scheme involves a target pursuit control law, which can be selected in switching or LQ optimal forms to regulate this distance estimate to zero. Besides formal analysis of the range estimation scheme and discussion of real-time implementations, the performance of the proposed control scheme is verified by simulation tests.

Keywords: Lyapunov methods, Cooperative control, Distributed control
Abstract: In this paper, the finite-time consensus problem for single integrator multi-agent systems with fixed and undirected communication topologies is investigated. A general class of event-triggered controllers is proposed and a rigorous stability analysis is provided, showing convergence of the error signals in a finite time. An upper bound on the settling time is given. Various examples showcasing controllers falling under the class of controllers proposed in this paper are presented. Simulations are provided to verify the theoretical results.

Keywords: Lyapunov methods, Safety critical systems, Switched systems
Abstract: Control barrier functions (CBFs) have recently been introduced as a systematic tool to ensure safety by establishing set invariance. When combined with a control Lyapunov function (CLF), they form a safety-critical control mechanism. However, the effectiveness of CBFs and CLFs is closely tied to the system model. In practice, model uncertainty can jeopardize safety and stability guarantees and may lead to undesirable performance. In this paper, we develop a safe learning-based control strategy for switching systems in the face of uncertainty. We focus on the case that a nominal model is available for a true underlying switching system. This uncertainty results in piecewise residuals for each switching surface, impacting the CLF and CBF constraints. We introduce a batch multi-output Gaussian process (MOGP) framework to approximate these piecewise residuals, thereby mitigating the adverse effects of uncertainty. A particular structure of the covariance function enables us to convert the MOGP-based chance constraints CLF and CBF into second-order cone constraints, which leads to a convex optimization. We analyze the feasibility of the resulting optimization and provide the necessary and sufficient conditions for feasibility. The effectiveness of the proposed strategy is validated through a simulation of a switching adaptive cruise control system.

Keywords: Lyapunov methods, Stability of nonlinear systems, Nonlinear system theory
Abstract: This paper deals with the interconnection between a linear system and a nonlinear operator consisting of asymmetric input backlash and asymmetric dead-zone. The uniform ultimate boundedness of the system is studied. A piecewise quadratic Lyapunov function, suitable with the polyhedral description of the nonlinear operator is proposed. The conservatism of existing results is therefore reduced. The effectiveness and improvement of the results are assessed using a numerical example.

Keywords: Lyapunov methods, Stability of nonlinear systems, Nonlinear system theory
Abstract: In this paper, the problem of partial stabilization of nonlinear systems along a given trajectory is considered. This problem is treated within the framework of stability of a family of sets. Sufficient conditions for the asymptotic stability of a one-parameter family of sets, using time-varying feedback control with trigonometric functions, are derived. The obtained results are applied to a model mechanical system.

Keywords: LMI's/BMI's/SOS's, Nonlinear system theory, Optimization algorithms
Abstract: This paper presents algorithms that upper-bound the peak value of a state function along trajectories of a continuous-time system with rational dynamics. The finite-dimensional but nonconvex peak estimation problem is cast as a convex infinite-dimensional linear program in occupation measures. This infinite-dimensional program is then truncated into finite-dimensions using the moment-Sum-of-Squares (SOS) hierarchy of semidefinite programs. Prior work on treating rational dynamics using the moment-SOS approach involves clearing dynamics to common denominators or adding lifting variables to handle reciprocal terms under new equality constraints. Our solution method uses a sum-of-rational method based on absolute continuity of measures. The Moment-SOS truncations of our program possess lower computational complexity and (empirically demonstrated) higher accuracy of upper bounds on example systems as compared to prior approaches.

Keywords: LMI's/BMI's/SOS's, Optimal control, UAV's
Abstract: We develop a convex relaxation for the minimum energy control problem of the well-known unicycle model in the form of a semidefinite program. Through polynomialization techniques, the infinite-dimensional optimal control problem is first reformulated as a non-convex, infinite-dimensional quadratic program which can be viewed as a trajectory generation problem. This problem is then discretized to arrive at a finite-dimensional, albeit, non-convex quadratically constrained quadratic program. By applying the moment relaxation method to this quadratic program, we obtain a sequence of semidefinite relaxations. We construct an approximate solution for the infinite-dimensional trajectory generation problem by solving the first- or second-order moment relaxation. A comprehensive simulation study provided in this paper suggests that the second-order moment relaxation is lossless.

Keywords: Electrical power systems, Fault detection and identification, Optimization
Abstract: Power system protection schemes today rely on currents rising by several orders of magnitude when faults occur. In inverter-dominated power systems, a fault current might be just a few percent larger than normal, making fault detection more difficult. One solution is for the inverter to slightly perturb its output current and/or voltage, i.e., to inject an auxiliary signal, so as to make the system's behavior under faults easier to distinguish from normal. In this paper, we optimize auxiliary signals for fault detection with distance relays. We begin with a standard auxiliary signal design problem for generic static systems. We use duality to reformulate the problem as a bilinear program, which we solve using the convex-concave procedure. We implement the framework in an example based on distance protection, in which the auxiliary signal is negative sequence current.

Keywords: Electrical power systems, Power plants, Predictive control for nonlinear systems
Abstract: In the metallurgical industry, Electrical Arc Furnace (EAF) are usually controlled through simple rules, without necessarily handling the coupling among their various components leading to inefficiency in the operation (e.g., instability in multi-phase control, unbalanced power distribution). Herein, we first develop a mathematical model of the EAF which is able to capture the behavior of the three-phase electrical evolution in time. Then, we formulate a mixed-integer optimal control problem in an MPC (Model Predictive Control) framework for the plant's linearized model. The goals are to concurrently control power and intensity across the three phases to track a priori given set points, handle integer inputs and limit transformer tap switching. This contributes to enhancing the furnace stability and energy efficiency. The approach is showcased over a furnace simulator developed by Eramet Ideas.

Keywords: Energy systems, Electrical power systems, Transportation systems
Abstract: Energy storage on all-electric vessels can enable shipboard-integrated power systems to meet increasing pulse load requirements and reduce fuel consumption and greenhouse gas emissions. However, integrating energy storage into shipboard power systems imposes additional requirements on the power management systems. In this paper, a power management controller is developed for the integrated power system utilizing a model predictive control (MPC) scheme. The MPC formulations developed here aim to manage the competing requirements of minimizing component health degradation and the power tracking error when responding to high ramp rate power demands. Sensitivity analysis is performed to explore the direct trade-off between generator health and battery health when meeting the load power demand is strictly enforced. The MPC solution is demonstrated for varying energy storage sizes to provide insight for the design and operation of an integrated shipboard power system.

Keywords: Identification, Signal processing, Electrical power systems
Abstract: This paper presents a novel frequency-based method to estimate the structure and parameters of a grid equivalent model by measuring currents and voltages at the terminals of the point of common coupling. This model is instrumental in calibrating the controller of grid connected power converters. The proposed approach can be generalized to arbitrary grid order, here the two most common equivalent grid structures are considered of second and forth order respectively. Additionally the method provides a measure of the quality of available data for estimation purpose, and therefore assessing the feasibility of the estimation with the given data-set.

Keywords: Electrical power systems, Adaptive control, Linear systems
Abstract: In this paper, we extend the design of data-adaptive retrofit control for power system stabilizers (PSS) from the one-axis generator model to the Park generator model, which is the most detailed representation of synchronous generators. The data-adaptive retrofit control allows individual sub-controller designers to design and implement PSS, using only a local subsystem model and local measurements. The data-adaptive PSS can also adapt to the variation of grid characteristics through the process of real-time estimation of intricate dynamical feedback effects between the main grid and the generator states. This paper presents the details of an extended retrofit control method that accounts for the higher order dynamics and increased parameters of the Park generator model. In addition, we perform a multi-objective optimization for online identification of grid characteristics to improve the performance of the PSS. Finally, we demonstrate the efficacy of the proposed data-adaptive PSS by conducting a detailed numerical simulation on the IEEE 68-bus power system model composed of the Park generator models, resulting in a large-scale complex nonlinear differential algebraic equation system.

Keywords: Electrical power systems, Optimization, Predictive control for linear systems
Abstract: This paper proposes a novel control algorithm to use bidirectional charging of electric vehicles (EVs) in the framework of vehicle-to-grid (V2G) technology for optimal energy transaction and investment. The energy storage components of an electric charging station, including the buffers of energy, provide the opportunity to sell energy to or buy energy from a smart grid that not only improves the stability and power quality of the grid, but also offers the possibility to the charging station owner and the EV drivers to benefit from the trades in the energy market financially. Therefore, a model predictive controller (MPC) is developed to maximize the profit of the charging station and satisfy the EVs minimum state of charge (SOC) requirement while participating in incentive-based ancillary programs of the grid. The proposed algorithm changes the energy investment in the components when the price of energy changes with time, especially the price of the grid’s energy, to keep the optimality of the energy shares. The simulation results confirm the effectiveness of the proposed control strategy.

Keywords: Intelligent systems, Neural networks, Manufacturing processes
Abstract: The high-speed, high-precision positioning of objects is a critical component in various industrial manufacturing processes. The semiconductor die packaging, for instance, requires the precise pickup and placement of semiconductor dies on substrates. This is done by coupling the silicon wafer which contains thousands of semiconductor dies, with a motion control platform equipped with linear motors and encoders. The motion controller relies on linear motors and encoders to accurately position the silicon wafer at reference positions, which are determined through the relative positions of the dies on the wafer. However, the challenge arises when neighboring dies get misaligned during the pickup process, making it impossible to read the position of the die through encoders. This paper addresses the challenge of precise alignment in high-speed, micro-scale manufacturing environments, where traditional methods struggle due to the disconnect between the point-of-interest (dies) and point-of-control (motor/silicon wafer). To overcome these challenges, we propose a Deep Neural Network (DNN) based perception that allows for robust sensing of die positions. We also propose a fusion mechanism to incorporate this vision feedback with the encoder to accurately detect the misalignment and compensate for it before periodic pickups of the dies. We use a software-in-the-loop validation framework to demonstrate that our proposed method could successfully eliminate the misalignment before the pickup in the range under consideration.

Keywords: Discrete event systems, Nonlinear system theory, Manufacturing processes
Abstract: This paper proposes an approach to find the eigenvalues and eigenvectors of a class of autonomous max-min-plus-scaling (MMPS) systems. First we show that time-invariant, monotone and non-expansive MMPS systems with only time variables has a unique structural eigenvalue and eigenvector under some conditions. Then, we propose a mixed-integer linear programming (MILP) algorithm to calculate the eigenvalue and the corresponding eigenvector for such systems. Finally, we present a modified linear programming (LP) algorithm to find all the eigenvalues of a general time-invariant MMPS system.

Keywords: Manufacturing processes, Fuzzy systems, Intelligent systems
Abstract: Melt pressure is a key indicator of melt flow stability and quality in polymer extrusion processes. The melt pressure level affects the degree of mixing and melt pressure stability which in turn influence the melt quality. Meanwhile, short-term melt pressure fluctuations result in dimensional variations in the extruded products in continuous extrusion processes. In industrial polymer extruders, the melt pressure is measured using pressure transducers installed close to the die entry. Process operators ensure the safe operation of the process based on these melt pressure measurements. This study proposes an intelligent control system based on the adaptive neuro-fuzzy inference system to manipulate the screw speed to maintain the melt pressure at a desired level while minimizing melt pressure fluctuations. Data recorded from an actual extrusion process was used to determine the parameters of the membership functions and the rule base of a Sugeno fuzzy inference system using neuro-fuzzy learning. The controller was designed and validated through simulation using Matlab Simulink. The results indicated that the proposed controller was capable of maintaining the desired melt pressure level while minimizing melt pressure fluctuations across different extrusion processing conditions, by manipulating the screw speed. Therefore, this will be an attractive solution to improve the dimensional stability and product quality of extruded products in continuous polymer extrusion processes.

Keywords: Process control, Chemical process control, Manufacturing processes
Abstract: The process manufacturing industry plays a pivotal role in the manufacturing industry. It is characterized by frequent fluctuations in inlet conditions, frequent changes in operating conditions, and numerous random disturbances. It is challenging to achieve the optimal operation of the whole process by ignoring the changes in operation status and only requiring the key technical indicator to be close to the optimal setpoint. To this end, this paper proposes an optimal control method for operation status migration in the process manufacturing industry. First, a method for defining and classifying operation statuses based on mechanism knowledge is proposed. Then, construct a nonlinear process description model and propose an online spatiotemporal recognition method for operation status. Finally, with the objectives of minimal consumption, system stability, and approach to the optimal setpoint, an optimal control model for optimal migration of operation statuses is constructed to obtain the optimal control quantities and achieve optimal operation. The experimental results show that the proposed method can reduce resource consumption by finding the optimal migration path of the operation status to ensure system stability and product quality, which is of great significance for actual production.

Keywords: Process control, Nonlinear system identification, Predictive control for nonlinear systems
Abstract: Microreactors are an essential part of modular chemical systems involved in the on-demand production of chemicals such as nanomaterials, pharmaceuticals, specialty chemicals, etc. Model-based nonlinear predictive control of microreactors is a challenging task due to the high online computational cost associated with developing and maintaining high-order first-principles nonlinear models. In this work, we propose a nonlinear data-driven model predictive control (NMPC) scheme for nanoparticle production in microreactors. In this paper, a non-linear Auto Regressive Exogenous Neural Network model (NARX-NN) is developed with the flow rates of the reactants as inputs and the peak value of the absorbance spectra (an indirect measure of the average size of nanoparticles) as output by performing a set of experiments in Corning Advanced-Flow^{TM} Reactors (AFR). Typically, producing a new desired average size nanoparticle on-demand is done by manual changes in the flow rates of reactants. In this work, a nonlinear model predictive controller using the identified NARX-NN model is formulated to track a change in the set point, the peak value of the spectra. The formulated controller with the identified NARX-NN model is demonstrated via the simulation studies. It is shown that the proposed NMPC with the NARX-NN model performs well in different scenarios of silver nanoparticle production.

Keywords: V&V of control algorithms, Process control, Stochastic systems
Abstract: This paper presents a new way to conduct the control performance assessment (CPA). The measurement of control quality is a multi-criteria task from a practical point of view. Generally, the tuning of any controller means reaching a compromise between the accuracy and speed. The required (optimal) ratio between these two contradictory factors depends on the process demands, limitations and the engineering skills. Two basic indexes: the overshoot and settling time fit perfectly into such defined requirements. This research follows these path, but with the use of modern measures: robust statistical scale and shape factors, tail index and ARFIMA filter fractional order estimator. The assessment uses two dimensional Index Ratio Diagram (IRD), which allow to compare contradictory measures. Moreover, they allow to define new multi-criteria index able to compare different loops. The validation is compared against commonly used integral indicators.

Keywords: Reduced order modeling, Linear time-varying systems, Hybrid systems
Abstract: In this work, we propose a characterization of the moments of a linear system for generalized filters that do not have an implicit representation, i.e., they do not satisfy a differential equation. We devote particular attention to the case in which the filter has a non-smooth convolution kernel. The notion of moment is extended using an integral matrix equation. We present a family of reduced-order models that achieves moment matching based on this generalized notion of moment. Finally, the developed results are demonstrated by means of a numerical example with smooth and non-smooth kernels.

Keywords: Reduced order modeling, Filtering
Abstract: In this paper we propose strategies to alleviate the impact of noise on data-driven model-order reduction by moment matching. We classify the noise affecting the data-driven methods as textit{interconnection noise} and textit{measurement noise}. We then consider two statistical models of the noise, namely Gaussian (white noise) and Student's t, to represent noise in a variety of applications. We propose and study the use of Wavelet denoising for dealing with white noise and the use of Huber regression for the Student's t-distribution. We demonstrate by means of extensive simulations how these strategies improve the accuracy and robustness of the data-driven algorithms.

Keywords: Reduced order modeling, Model/Controller reduction
Abstract: This paper proposes a model reduction approach for simplifying the interconnection topology of Lur'e network systems. A class of reduced-order models are generated by the projection framework based on graph clustering, which not only preserve the network structure but also ensure absolute stability. Furthermore, we provide an upper bound on the input-output approximation error between the original and reduced-order Lur'e network systems, which is expressed as a function of the characteristic matrix of graph clustering. Finally, the results are illustrated via a numerical example.

Keywords: Linear parameter-varying systems, Large-scale systems, Computer aided control design
Abstract: The control of nonlinear large-scale dynamical models such as the incompressible Navier-Stokes equations is a challenging task. The computational challenges in the controller design come from both the possibly large state space and the nonlinear dynamics. A general purpose approach certainly will resort to numerical linear algebra techniques which can handle large system sizes or to model order reduction. In this work we propose a two-folded model reduction approach tailored to nonlinear controller design for incompressible Navier-Stokes equations and similar PDE models that come with quadratic nonlinearities. Firstly, we approximate the nonlinear model within in the class of LPV systems with a very low dimension in the parametrization. Secondly, we reduce the system size to a moderate number of states. This way, standard robust LPV theory for nonlinear controller design becomes feasible. We illustrate the procedure and its potentials by numerical simulations.

Keywords: Reduced order modeling, Linear systems
Abstract: In this work, we present the standard Singular Perturbations technique applied to Implicit port-Hamiltonian systems. The investigation produces a structure-preserving reduced-order model if certain additional passivity conditions are satisfied. Moreover, such an investigation provides a differ- ent insight into the standard Singular Perturbations approach relating the negligible time constant parameters ε to energy parameters. We analyze the deviation between the complete system model and the reduced one via a Lyapunov-based approach. We then conclude the paper by applying the proposed reduced order model to a DC-motor example to show the effectiveness of the development.

Keywords: Nonlinear system identification, Reduced order modeling, Automata
Abstract: In this paper, a parameter identification method for multilinear time-invariant grey-box models which are structured by Boolean functions is presented. Applying binary indexing to network graphs enables an efficient extraction of structural system information which can be transformed into continuous Zhegalkin polynomials. Their parameters are identified with a nonnegative alternating least squares (ALS)-algorithm. A running example demonstrates the application of this method and the algorithm is briefly analysed.

Keywords: Machine learning, Stochastic systems, Statistical learning
Abstract: We consider a sequential stochastic multi-armed bandit problem where the agent interacts with the bandit over multiple episodes. The reward distribution of the arms remains constant throughout an episode but can change over different episodes. We propose an algorithm based on UCB to transfer the reward samples from the previous episodes and improve the cumulative regret performance over all the episodes. We provide regret analysis and empirical results for our algorithm, which show significant improvement over the standard UCB algorithm without transfer.

Keywords: Machine learning, Identification, Energy systems
Abstract: Recent research literature shows that system identification techniques can be successfully combined with machine learning to improve the accuracy of the models obtained. In this context, the contribution of this work builds upon a research line that combines the Regression Trees method with AutoRegressive eXogenous identification to derive models of dynamical systems exploiting historical data. The main contribution of this paper is to formally relate such methodology with the scenario approach framework, thus providing probabilistic guarantees on the derived model. The proposed method is validated on a real experimental setup: first a comparison in terms of accuracy with the former method - which does not provide probabilistic guarantees - is provided, then the effectiveness of the derived probabilistic guarantees is validated on the testing dataset from our experimental setup.

Keywords: Machine learning, Markov processes, Optimal control
Abstract: We investigate the convergence properties of Temporal Difference (TD) Learning on Markov Reward Processes (MRPs) with new structures for incorporating hidden state information. In particular, each state is characterized by both observable and hidden components, with the assumption that the observable and hidden parts are statistically independent. This setup differs from Hidden Markov Models and Partially Observable Markov Decision Models, in that here it is not possible to infer the hidden information from the state observations. Nevertheless, the hidden state influences the MRP through the rewards, rendering the reward sequence non-Markovian. We prove that TD learning, when applied only on the observable part of the states, converges to a fixed point under mild assumptions on the step-size. Furthermore, we characterize this fixed point in terms of the statistical properties of both the Markov chains representing the observable and hidden parts of the states. Beyond the theoretical results, we illustrate the novel structure on two application setups in communications. Furthermore, we validate our results through experimental evidence, showcasing the convergence of the algorithm in practice.

Keywords: Machine learning, Optimization algorithms, Optimization
Abstract: This work presents a new approach to solve the sparse linear regression problem, i.e., to determine a 𝑘 -sparse vector 𝑤∈𝑅^𝑑 that minimizes the cost ∥𝑦−𝐴𝑤∥2^2 . In contrast to the existing methods, our proposed approach splits this 𝑘 -sparse vector into two parts --- (a) a column stochastic binary matrix 𝑉 , and (b) a vector 𝑥∈𝑅^𝑘 . Here, the binary matrix 𝑉 encodes the location of the 𝑘 non-zero entries in 𝑤 . Equivalently, it encodes the subset of 𝑘 columns in the matrix 𝐴 that map 𝑤 to 𝑦 . We demonstrate that this enables modeling several non-trivial application specific structural constraints on 𝑤 as constraints on 𝑉 . The vector 𝑥 comprises of the actual non-zero values in 𝑤 . We use Maximum Entropy Principle (MEP) to solve the resulting optimization problem. In particular, we ascribe a probability distribution to the set of all feasible binary matrices 𝑉 , and iteratively determine this distribution and the vector 𝑥 such that the associated Shannon entropy gets minimized, and the regression cost attains a pre-specified value. The resulting algorithm employs homotopy from the convex entropy function to the non-convex cost function to avoid poor local minimum. We demonstrate the efficacy and flexibility of our proposed approach in incorporating a variety of practical constraints, that are otherwise difficult to model using the existing benchmark methods.

Keywords: Machine learning, Optimization, Safety critical systems
Abstract: Recently, bandit optimization has received significant attention in real-world safety-critical systems that involve repeated interactions with humans. While there exist various algorithms with performance guarantees in the literature, practical implementation of the algorithms has not received as much attention. This work presents a comprehensive study on the computational aspects of safe bandit algorithms, specifically safe linear bandits, by introducing a framework that leverages convex programming tools to create computationally efficient policies. In particular, we first characterize the properties of the optimal policy for safe linear bandit problem and then propose an end-to-end pipeline of safe linear bandit algorithms that only involves solving convex problems. We also numerically evaluate the performance of our proposed methods.

Keywords: Optimization algorithms, Neural networks, Machine learning
Abstract: The success of deep learning over the past decade mainly relies on gradient-based optimisation and backpropagation. This paper focuses on analysing the performance of first-order gradient-based optimisation algorithms, gradient descent and proximal gradient, with time-varying non-convex cost function under (proximal) Polyak-{L}ojasiewicz condition. Specifically, we focus on using the forward mode of automatic differentiation to compute gradients in fast-changing problems where calculating gradients using the backpropagation algorithm is either impossible or inefficient. Upper bounds for tracking and asymptotic errors are derived for various cases, showing the linear convergence to a solution or a neighbourhood of an optimal solution, where the convergence rate decreases with the increase in the dimension of the problem. We present numerical results demonstrating the method's correctness and performance.

Keywords: Linear systems, Optimal control, Robust control
Abstract: Despite the celebrated success of linear quadratic Gaussian control (LQG) for stochastic systems, LQG approaches are inefficient in handling systems with non-Gaussian noises. This paper is concerned with linear quadratic control of discrete-time systems with bounded noises and unobservable system states. We describe such noises and system states by ellipsoidal sets, enabling the establishment of boundaries for those uncertainties in the control. Further, we learn and update the ellipsoidal sets for the system states by an ellipsoidal set-membership filter. With the learned ellipsoidal sets, we derive a robust state-feedback optimal control law by solving a rendered semidefinite programming problem. Simulation results demonstrate the enhanced control performance by the proposed method.

Keywords: Linear systems, Optimal control
Abstract: In this paper, we have explored the possibility to exploit control theory to introduce new steering techniques for atomic clocks of arbitrary order. Current studies show that most of the well-known steering methods are developed for the two-state atomic clock model and not much attention is paid to find efficient steering methods for higher-order atomic clocks. We introduce two novel clock steering methods for atomic clocks: one based on output stabilization problem and the other based on frequency deviation regulation problem. First, we prove the existence of control laws for the solvability of these two problems for atomic clocks. Second, to find a suitable control law for both steering problems, we outlined efficient procedures to obtain gain matrices and used the Kalman filter for state estimates. A few numerical examples have been given to analyze the performance of the proposed control approaches.

Keywords: Linear systems
Abstract: Bounding or diminishing the number of sign changes and local extrema in a signal is an intrinsic system property in, e.g., low-pass filtering or the over- and undershooting behaviour in the step-response of controlled systems. This work shows how to verify these properties for the observability/controllability operator of a linear time-invariant system under strict external positivity of a set of compound systems, which relaxes/generalizes the standard external positivity notion. In contrast to earlier work, the presented approach is significantly less dependent on a particular realization. The results are demonstrated by bounding the number of sign changes in an impulse response and, thus, the number of local extrema in the step response.

Keywords: Linear parameter-varying systems, Identification, Neural networks
Abstract: Ensuring stability of discrete-time (DT) linear parameter-varying (LPV) input-output (IO) models estimated via system identification methods is a challenging problem as known stability constraints can only be numerically verified, e.g., through solving Linear Matrix Inequalities. In this paper, an unconstrained DT-LPV-IO parameterization is developed which gives a stable model for any choice of model parameters. To achieve this, it is shown that all quadratically stable DT-LPV-IO models can be generated by a mapping of transformed coefficient functions that are constrained to the unit ball, i.e., a small-gain condition. The unit ball is then reparameterized through a Cayley transformation, resulting in an unconstrained parameterization of all quadratically stable DT-LPV-IO models. As a special case, an unconstrained parameterization of all stable DT linear time-invariant transfer functions is obtained. Identification using the stable DT-LPV-IO model with neural network coefficient functions is demonstrated on a simulation example of a parameter-varying mass-damper-spring system.

Keywords: Stability of linear systems, Optimization, Computational methods
Abstract: The robustness of the stability properties of dynamical systems in the presence of unknown/adversarial perturbations to system parameters is a desirable property. In this paper, we present methods to efficiently compute and improve the approximate stability radius of linear time-invariant systems. We propose two methods to derive closed-form expressions of approximate stability radius, and use these to re-design the system matrix to increase the stability radius. Our numerical studies show that the approximations work well and are able to improve the robustness of the stability of the system.

Keywords: Linear time-varying systems, Observers for linear systems, Lyapunov methods
Abstract: An implicit Euler discretization scheme of the prescribed-time converging observer from [Holloway&Krstic,2019] for a second order system is given, which preserves all main properties of the continuous-time counterpart, and can be recursively applied on any interval of time. In addition, the estimation error stays bounded in the presence of bounded measurement noise. The efficiency of the suggested differentiator is illustrated through numeric experiments.

Keywords: Modeling, Differential algebraic systems
Abstract: This study provides a systematic description and results of a dynamical simulation model of a rotary kiln for clinker, based on first engineering principles. The model is built upon thermophysical, chemical, and transportation models for both the formation of clinker phases and fuel combustion in the kiln. The model is presented as a 1D model with counter-flow between gas and clinker phases and is demonstrated by a simulation using industrially relevant input. An advantage of the proposed model is that it provides the evolution of the individual compounds for both the fuel and clinker. As such, the model comprises a stepping stone for evaluating the development of process control systems for existing cement plants.

Keywords: Nano systems
Abstract: This paper is about feedback control system for Scanning Tunneling Microscopy (STM). The system aims to maintain a constant tunneling current between tip and sample surface, despite external disturbances. Four controllers are tested and compared, resuming three previously considered techniques, and including a novel genetic-algorithm-based approach. Results in particular highlight the effectiveness of the latter in maintaining stability and rejecting disturbances, and the study includes both simulation and experimental results. The findings hence contribute to the development of reliable control systems for nanoscale imaging and manipulation.

Keywords: Quantum control, Stochastic systems, Optimization
Abstract: This paper is concerned with open quantum systems whose dynamic variables have an algebraic structure, similar to that of the Pauli matrices for finite-level systems. The Hamiltonian and the operators of coupling of the system to the external bosonic fields depend linearly on the system variables. The fields are represented by quantum Wiener processes which drive the system dynamics according to a quasilinear Hudson-Parthasarathy quantum stochastic differential equation whose drift vector and dispersion matrix are affine and linear functions of the system variables. This setting includes the zero-Hamiltonian isolated system dynamics as a particular case, where the system variables are constant in time, which makes them potentially applicable as a quantum memory. In a more realistic case of nonvanishing system-field coupling, we define a memory decoherence time when a mean-square deviation of the system variables from their initial values becomes relatively significant as specified by a weighting matrix and a fidelity parameter. We consider the decoherence time maximization over the energy parameters of the system and obtain a condition under which the zero Hamiltonian provides a suboptimal solution. This optimization problem is also discussed for a direct energy coupling interconnection of such systems.

Keywords: MEMS, Identification
Abstract: MEMS gyroscopes are generally made up of two resonant systems: the so-called drive and sense modes. It is well known that the tracking of the drive-mode resonance frequency is crucial to make the device operate accurately. In this paper, we propose an approach based on recursive identification that allows to estimate this resonance frequency over the time. The proposed approach pertains to a recently developed control configuration which is based on the Hinf control framework and allows this configuration to give satisfactory control performance even when the drive-mode resonance frequency changes due to environment effects.

Keywords: Quantum control, Concensus control and estimation, Quantum information and control
Abstract: In this study, we deal with quantum networked systems consisting of N quantum subsystems having D states. In previous studies, it was proved that a symmetric-state consensus (SSC) can be attained by an algorithm composed of local measurements and a local feedback control in cases of D=2 and 3. However, a proof for general D remained an open problem. In this study, we rigorously prove that an SSC can be attained by the same algorithm in the case of general D.

Keywords: V&V of control algorithms, Robotics, Emerging control theory
Abstract: During the execution of periodic motions, such as locomotion, animals exploit the elasticity in their body to increase efficiency. Adding elasticity in robotic systems, e.g., through a Series Elastic Actuator (SEA), enables to mimic this biological solution by storing energy in the spring. The standard strategy to efficiently drive such systems in periodic motions is to assume the motor static such that the SEA behaves like a single-mass-spring system, excited at its natural frequency. However, when regarding the SEA as a two-mass-spring system, we can derive another control strategy to excite periodic oscillations, where the motor and link inertia exhibit anti-phasic oscillations. This paper compares these two control strategies on a hardware SEA test bed regarding performance metrics such as maximal input torque and electrical power consumption. The control objective for this comparison is to excite a link oscillation with a desired amplitude, as could be needed for a pick-and-place task. We find that less current is needed for the given task and hardware for the first control strategy. The second strategy causes more friction that needs compensation but also increases stored system energy for the desired amplitude. When adding motor inertia shaping to this second strategy, we find a flexible controller that can shift the system to either behave like a single- or two-mass-spring system. Thus, we propose a promising control approach that can adapt system behavior to best suit a given oscillatory task.

Keywords: Safety critical systems, Stochastic control, Robotics
Abstract: Uncertainties arising in various control systems, such as robots that are subject to unknown disturbances or environmental variations, pose significant challenges for ensuring system safety, such as collision avoidance. At the same time, safety specifications are getting more and more complex, e.g., by composing multiple safety objectives through Boolean operators resulting in non-smooth descriptions of safe sets. Control Barrier Functions (CBFs) have emerged as a control technique to provably guarantee system safety. In most settings, they rely on an assumption of having deterministic dynamics and smooth safe sets. This paper relaxes these two assumptions by extending CBFs to encompass control systems with stochastic dynamics and safe sets defined by non-smooth functions. By explicitly considering the stochastic nature of system dynamics and accommodating complex safety specifications, our method enables the design of safe control strategies in uncertain and complex systems. We provide formal guarantees on the safety of the system by leveraging the theoretical foundations of stochastic CBFs and non-smooth safe sets. Numerical simulations demonstrate the effectiveness of the approach in various scenarios.

Keywords: Communication networks, Safety critical systems, Complex systems
Abstract: In this paper we propose an unpredictability-based jamming defense framework based on the principles of Moving Target Defense for a wireless communication problem. Taking advantage of the complex nature of large-scale cyber-physical systems, we consider a platform consisting of a single receiving component but multiple potential transmitting components, each equipped with a multi-antenna phased array. We formulate an optimization problem over the probability simplex that characterizes a randomized receiving angle which seeks to balance between the estimated performance of the transmission and an entropy-based unpredictability measure. Furthermore, we explore the effect of an intelligent adversary that has knowledge of the derived probabilities and optimally places a single-antenna jamming device to disrupt the communication links. Finally, simulation results showcase the efficacy of the proposed algorithm.

Keywords: Safety critical systems, Autonomous robots, Robotics
Abstract: In fields such as mining, search and rescue, and archaeological exploration, ensuring real-time, collision-free navigation of robots in confined, cluttered environments is imperative. Despite the value of established path planning algorithms, they often face challenges in convergence rates and handling dynamic infeasibilities. Alternative techniques like collision cones struggle to accurately represent complex obstacle geometries. This paper introduces a novel category of control barrier functions, known as Polygonal Cone Control Barrier Function (PolyC2BF), which addresses overestimation and computational complexity issues. The proposed PolyC2BF, formulated as a Quadratic Programming (QP) problem, proves effective in facilitating collision-free movement of multiple robots in complex environments. The efficacy of this approach is further demonstrated through PyBullet simulations on quadruped (unicycle model), and crazyflie 2.1 (quadrotor model) in cluttered environments.

Keywords: Safety critical systems, Delay systems, Robust control
Abstract: This paper proposes a Control Barrier Function (CBF)-based delay adaptive controller design to accomplish robust safety in the presence of unknown but bounded constant input delay. To this end, we first estimate the input delay by using a gradient descent method minimizing the discrepancy between the current state and the estimated state. Then, we establish the state prediction feedback with the estimated input delay, which is leveraged to attenuate the effect of the input delay. However, due to the error between the true delay and the estimated delay, there is a state prediction error that leads to violations of safety if we use the normal CBFs. To remedy this, we use ideas from Measurement Robust Control Barrier Functions (MRCBFs) that enforce the robust safety constraint against the state prediction error. Specifically, we bound the state prediction error in connection with the input delay estimation error and incorporate the worst case error bound into the safety constraint. The proposed method is verified in the simulations under the connected automated vehicles scenario.

Keywords: Safety critical systems, Markov processes, Uncertain systems
Abstract: In this paper, we consider the problem of safety assessment for Markov decision processes without explicit knowledge of the model. We aim to learn probabilistic safety specifications associated with a given policy without compromising the safety of the process. To accomplish our goal, we characterize a subset of the state-space namely proxy set, which contains the states that are near in a probabilistic sense to the forbidden set consisting of all unsafe states. We compute the safety function using the single-step temporal difference method. To this end, we relate the safety function computation to that of the value function estimation using temporal difference learning. Since the given control policy could be unsafe, we use a safe baseline sub-policy to generate data for learning. We then use an off-policy temporal difference learning method with importance sampling to learn the safety function corresponding to the given policy. Finally, we demonstrate our results using a numerical example.

Keywords: Safety critical systems, Large-scale systems, LMI's/BMI's/SOS's
Abstract: This paper proposes a (control) barrier function synthesis and safety verification scheme for interconnected nonlinear systems based on assume-guarantee contracts (AGC) and sum-of-squares (SOS) techniques. It is well-known that the SOS approach does not scale well for barrier function synthesis for high-dimensional systems. In this paper, we show that compositional methods like AGC can mitigate this problem. We formulate the synthesis problem into a set of small-size problems, which constructs local contracts for subsystems, and propose a negotiation scheme among the subsystems at the contract level. The proposed scheme is then implemented numerically on two examples: vehicle platooning and room temperature regulation.

Keywords: Automotive, Autonomous systems, Constrained control
Abstract: This paper revisits the convex lifting method for space partition, emphasizing the generation of safe corridors in the context of navigation with obstacle avoidance guarantees. This enables the agent to navigate within the designated corridors, disregarding obstacles. The paper also emphasizes that the method can be adapted to a highway scenario, particularly in the context of overtaking maneuvers.

Keywords: Automotive, Power electronics, Energy systems
Abstract: In this work, a switching regulator is developed based on a quadratic-based step-down converter combined with an input filter that provides a wide conversion ratio of output voltage and non-pulsating input current; thus, it is suitable as an interface between lithium-ion batteries and on-board applications of hybrid electric vehicles. Simple and easy-to-use expressions for the proper selection of the converter and the robust controller parameters are obtained, and a brief discussion about the LC filter added to the input port may cause closed-loop instability. Test results in a laboratory prototype's time and frequency domains with an output power of 300 W are shown.

Keywords: Automotive, Robotics, Autonomous systems
Abstract: Incorporating smart ports for logistics efficiency is a trend in developed nations, prompting similar endeavors in emerging markets like Indonesia. Manual docking of container trucks in docking stations is challenging and time-consuming, requiring skilled operators. This project aims to realize an autonomous precision docking system to enhance efficiency and accuracy. The container truck utilizes hydraulic-based motors, with electric signals coordinating propulsion, steering, braking, and lifting. A cabin-based drive-by-wire controller integrated with sensors and electrical circuits, to inject signaling into existing actuators, enable autonomous monitoring and control. Trials showcase accurate and precise dockings, aiming to alleviate logistic distribution challenges, enhancing Indonesia’s maritime industry.

Keywords: Machine learning, Autonomous systems, Identification
Abstract: Accurately modelling the dynamics of heavy duty vehicles such as trucks is essential for safe autonomous navigation. The dynamical model needs to capture complex system behaviour in various weather and road conditions as well as under different load configurations. This abstract outlines the integration of Physics-informed Long Short-Term Memory (PI-LSTM) networks as dynamical models within the context motion planning and control for autonomous vehicles. By leveraging the predictive capabilities of LSTMs to model complex dynamics, and the generalizability imposed by adding the physics constraints in the loss function, we propose a framework for generating more efficient and reliable predictions that are tailored for motion planning and control.

Keywords: Aerospace, V&V of control algorithms, Cooperative control
Abstract: We present a prototype of the Hardware-In-the- Loop (HIL) testbed for precise spacecraft formation flight control. The testbed serves as a platform to verify and validate the multifaceted aspects of interferometry formation flight control systems. The prototype demonstrates the three essential features of the HIL testbed: laser link acquisition, laser locking, and interferometry relative position control, by integrating the Michelson laser interferometer onto the 6-axis motion stage. We conduct an experiment of two-spacecraft formation control to ascertain the efficacy of the prototype in demonstrating these features.

Keywords: V&V of control algorithms, Modeling, Aerospace
Abstract: This short paper describes a new way to perform GNC closed loop testing in the FM hardware for the ESA PROBA3 mission. Unfortunately, a full featured GNC-SCOE is not available. It has been found a way to close the loop with some logic being executed inside the flight computer. We call this product “miniDKE’. This approach requires almost null modification of the existing flight software. The validation of the miniDKE is incourse and we expect to perform the testing of the critical modes in the PROBA3 satellites soon.

Keywords: Mechatronics, Power electronics, Manufacturing processes
Abstract: This tutorial session introduces the context and the roadmap towards closing the gap between academic research and industrial practice in automatic control.

The session will include the following:

Introduction to the innovation framework (Silvia Mastellone) Case Study: Robotics and Manufacturing Automation (Alisa Rupenyan) Case Study: Energy & Power Conversion (Pieder Jörg) Introduction of Control for Societal-Scale Challenges: Roadmap 2030 (Tariq Samad) Interactive panel discussion (Silvia Mastellone, Tariq Samad, Alisa Rupenyan, Pieder Jörg, Efe Balta)

Keywords: Adaptive systems, Observers for linear systems, Uncertain systems
Abstract: In this paper, an adaptive observer is designed, which addresses the problem of joint estimation of state and the unknown parameters that emerge in the system dynamics as well as the output equation of a multi-input multi-output (MIMO) plant. Conventional adaptive observers ensure parameter convergence when the input-output signals are significantly `energy-rich' to satisfy the persistence of excitation (PE). The proposed work develops a two-layered filter adaptive observer architecture based on initial excitation (IE), which relaxes the stringent PE condition in terms of excitation requirement and online verifiability. The unknown initial condition of the state is strategically appended with the vector of the unknown parameter, which essentially results in a higher dimensional estimator. This extended parameter estimator proves to be instrumental in ensuring uniformly global exponential stability (UGES) of the estimation error, applicable in a delayed sense. As far as the authors are aware this is the first work where a `relaxed' excitation condition is utilized for simultaneous estimation of states and the unknown parameters which appear both in the state dynamics and the output. To validate the efficacy of the performance of the adaptive observer proposed, a simulation study has been undertaken on a remotely piloted aircraft model.

Keywords: Robust adaptive control, Optimal control, Robotics
Abstract: While exact parameter estimation is desired in numerous applications, many adaptive controllers require the fulfillment of the persistency of excitation (PE) condition to achieve that objective. Relaxing the PE condition poses a challenging theoretical problem and many research works have been devoted to addressing this issue. In this paper, we propose a novel adaptive robust nonlinear H-infinity optimal controller for trajectory tracking of mechanical systems subjected to unknown parameters and external disturbances. The proposed method includes an additional term, based on the Dynamic Regressor Extension and Mixing (DREM) approach, into the adaptive law. This modification enables exact parameter estimation even without fulfilling the PE condition. The effectiveness of the proposed adaptive robust nonlinear H-infinity optimal controller is corroborated through numerical experiments involving a simplified CRS-A465 robot manipulator. Comparison analyses are carried out with a classic adaptive nonlinear H-infinity controller.

Keywords: Adaptive systems
Abstract: The problem of estimation in the linear regression model is studied under the hypothesis that the noise is sufficiently small comparing to the regressor. Then the estimation solution is searched for a new regression containing the powers of the unknown parameters and disturbance, where the influence of the latter is attenuated. It is shown that power transformation of a regressor can preserve the excitation under mild assumptions. Possibilities of evaluation of powers of parameters are investigated using the dynamic regression extension and mixing (DREM) method. The performance of the estimators is demonstrated in numerical experiments.

Keywords: Safety critical systems, Robust adaptive control, Uncertain systems
Abstract: Safe control with guarantees generally requires the system model to be known. It is far more challenging to handle systems with uncertain parameters. In this paper, we propose a generic algorithm that can synthesize and verify safe controllers for systems with constant, unknown parameters. In particular, we use robust-adaptive control barrier functions (raCBFs) to achieve safety. We develop new theories and techniques using sum-of-squares that enable us to pose synthesis and verification as a series of convex optimization problems. In our experiments, we show that our algorithms are general and scalable, applying them to three different polynomial systems of up to moderate size (7D). Our raCBFs are currently the most effective way to guarantee safety for uncertain systems, achieving 100% safety and up to 55% performance improvement over a robust baseline.

Keywords: Fault diagnosis, Robust adaptive control, Stability of linear systems
Abstract: A method for designing inputs in active fault diagnosis combined with a robust model reference adaptive controller is proposed in this paper. The approach ensures that the output of a plant with unknown parameters converges to a reference model during the diagnosis process. The paper proves that Lyapunov stability can be guaranteed, given a condition on the parameters of the robust adaptive law. Additionally, a novel set-membership filter, employing constrained zonotopes, is introduced. This filter is computationally efficient and does not require outer approximations. The active fault diagnosis is accomplished by generating reference inputs online, allowing the output sets to separate for different plant models. A demonstration of the proposed method on a numerical example is provided in the final.

Keywords: Predictive control for linear systems, Optimal control, Uncertain systems
Abstract: We consider control of uncertain linear time-varying stochastic systems from the perspective of regret minimization. Specifically, we focus on the problem of designing a feedback controller that minimizes the loss relative to a clairvoyant optimal policy that has foreknowledge of both the system dynamics and the exogenous disturbances. In this competitive framework, establishing robustness guarantees proves challenging as, differently from the case where the model is known, the clairvoyant optimal policy is not only inapplicable, but also impossible to compute without knowledge of the system parameters. To address this challenge, we embrace a scenario optimization approach, and we propose minimizing regret robustly over a finite set of randomly sampled system parameters. We prove that this policy optimization problem can be solved through semidefinite programming, and that the corresponding solution retains strong probabilistic out-of-sample regret guarantees in face of the uncertain dynamics. Our method naturally extends to include satisfaction of safety constraints with high probability. We validate our theoretical results and showcase the potential of our approach by means of numerical simulations.

Keywords: Predictive control for nonlinear systems, Autonomous systems, Robust control
Abstract: Ensuring collision avoidance is a critical challenge for autonomous vehicles, particularly when faced with uncertain moving obstacles. This work presents a robust collision avoidance framework, integrating data-driven reachability analysis with Model Predictive Control (MPC). The framework is specifically designed to address scenarios where detailed information about the moving obstacles that should be avoided is unavailable. A data-driven approach is employed, which utilizes uncertain measurements corrupted by bounded noise of the obstacle. Based on the measurements, an over-approximation of the reachable sets by moving obstacles represented as zonotopes is constructed. To guarantee security, a safety margin is added to the approximation. The resulting set is employed as a polytopic collision avoidance constraint within the robust MPC scheme, enabling effective control of the autonomous vehicle while guaranteeing avoidance of impacts. The effectiveness of the data-driven collision avoidance scheme is demonstrated through extensive simulations. The presented results outline a promising advancement in collision avoidance for autonomous vehicles operating in uncertain environments.

Keywords: Predictive control for nonlinear systems, Computational methods, Optimization algorithms
Abstract: In recent literature, it has been shown that the number of steps in a sequential quadratic programming algorithm for a non-linear model predictive control (NMPC) problem can be greatly reduced by a parallel shooting method. The efficiency of such a parallel shooting method further depends on how the algorithm is implemented on parallel computing platforms such as Graphics Processing Units (GPUs). The GPU implementation should consider the degree of parallelism necessary for higher time efficiency as well as the hardware resource consumption/limitation at the GPU for a given problem size. In this paper, we present a multilevel parallel GPU implementation for sequential quadratic programming and an (Alternating Direction Method of Multipliers) ADMM solver. First, we introduce a GPU implementation enabling parallel computing of many quadratic programs (QPs) by functional parallelism. Next, we parallelize each QP solver using data parallelism of basic linear matrix operations. We show that the proposed GPU implementation greatly scales with the degree of parallelism in the parallel shooting method. Further, we show how a GPU implementation can be configured for a given problem size avoiding resource overprovisioning.

Keywords: Predictive control for nonlinear systems, Machine learning, Optimization algorithms
Abstract: Model predictive control can optimally deal with nonlinear systems under consideration of constraints. The control performance depends on the model accuracy and the prediction horizon. Recent advances propose to use reinforcement learning applied to a parameterized model predictive controller to recover the optimal control performance even if an imperfect model or short prediction horizons are used. However, common reinforcement learning algorithms rely on first order updates, which only have a linear convergence rate and hence need an excessive amount of dynamic data. Higher order updates are typically intractable if the policy is approximated with neural networks due to the large number of parameters.

In this work, we use a parameterized model predictive controller as policy, and leverage the small amount of necessary parameters to propose a trust-region constrained Quasi-Newton training algorithm for policy optimization with a superlinear convergence rate. We show that the required second order derivative information can be calculated by the solution of a linear system of equations. A simulation study illustrates that the proposed training algorithm outperforms other algorithms in terms of data efficiency and accuracy.

Keywords: Predictive control for nonlinear systems, Neural networks, Identification
Abstract: In order to accurately model the dynamics of nonlinear electro-mechanical systems, it is imperative to consider the contributions of coupling terms and dissipation. The Lagrangian formulation alone is insufficient to fully capture the holistic behavior of the system. Coupling and dissipation mechanisms play a pivotal role in shaping the system's response. Consequently, to effectively capture the dynamics of inter-coupled electro-mechanical systems with dissipation, we propose an extended Lagrangian-informed deep neural network framework in this paper. Our approach leverages the underlying physics-based knowledge of the system, incorporating it into the neural network architecture. By employing the Euler-Lagrange equations as constraints in the training process, we ensure that the learned dynamics conform to the true behavior of the system. To validate the theoretical framework, we conduct simulation experiments on a DC motor with a cart system, which serves as a representative model of dissipative nonlinear electro-mechanical systems. The experimental results demonstrate the efficacy of our approach in accurately capturing and integrating the dynamics to solve the reference tracking model predictive control design.

Keywords: Optimization algorithms, Decentralized control, Distributed cooperative control over networks
Abstract: In this paper, we address the distributed optimization problem over unidirectional networks with possibly time-invariant heterogeneous bounded transmission delays. In particular, we propose a modified version of the Accelerated Distributed Directed OPTimization (ADD-OPT) algorithm, herein called Robustified ADD-OPT (R-ADD-OPT), which is able to solve the distributed optimization problem, even when the communication links suffer from heterogeneous but bounded transmission delays. We show that if the gradient step-size of the R-ADD-OPT algorithm is within a certain range, which also depends on the maximum time delay in the network, then the nodes are guaranteed to converge to the optimal solution of the distributed optimization problem. The range of the gradient step-size that guarantees convergence can be computed a priori based on the maximum time delay in the network.

Keywords: Optimization algorithms, Machine learning
Abstract: Federated learning is a distributed learning framework that allows a set of clients to collaboratively train a model under the orchestration of a central server, without sharing raw data samples. Although in many practical scenarios the derivatives of the objective function are not available, only few works have considered the federated zeroth-order setting, in which functions can only be accessed through a budgeted number of point evaluations. In this work we focus on convex optimization and design the first federated zeroth-order algorithm to estimate the curvature of the global objective, with the purpose of achieving superlinear convergence. We take an incremental Hessian estimator whose error norm converges linearly in expectation, and we adapt it to the federated zeroth-order setting, sampling the random search directions from the Stiefel manifold for improved performance. Both the gradient and Hessian estimators are built at the central server in a communication-efficient and privacy-preserving way by leveraging synchronized pseudo-random number generators. We provide a theoretical analysis of our algorithm, named FedZeN, proving local quadratic convergence with high probability and global linear convergence up to zeroth-order precision. Numerical simulations confirm the superlinear convergence rate and show that our algorithm outperforms the federated zeroth-order methods available in the literature.

Keywords: Optimization algorithms, Optimization, Optimal control
Abstract: This paper proposes an almost feasible Sequential Linear Programming (afSLP) algorithm. In the first part, the practical limitations of previously proposed Feasible Sequential Linear Programming (FSLP) methods are discussed along with illustrative examples. Then, we present a generalization of FSLP based on a tolerance-tube method that addresses the shortcomings of FSLP. The proposed algorithm afSLP consists of two phases. Phase I starts from random infeasible points and iterates towards a relaxation of the feasible set. Once the tolerance-tube around the feasible set is reached, phase II is started and all future iterates are kept within the tolerance-tube. The novel method includes enhancements to the originally proposed tolerance-tube method that are necessary for global convergence. afSLP is shown to outperform FSLP and the state-of-the-art solver IPOPT on a SCARA robot optimization problem.

Keywords: Optimization algorithms, Optimization, Communication networks
Abstract: This paper investigates the weapon-target assignment problem and a distributed neighborhood search algorithm is proposed to solve this problem. The proposed algorithm is developed based on the very large-scale neighborhood search (VLSN) algorithm, which is originally developed for centralized allocation among agents. We improve the construction of the improvement graph and the search process for valid cycles in the VLSN algorithm. This enables that the allocation algorithm can be deployed in a distributed way. Each missile maintains a local improvement graph by communicating with its neighbors and attempts to search for valid cycles. The valid cycle directs missiles to exchange their attack targets to achieve the distributed target assignment. Extensive numerical simulations demonstrate the effectiveness of the method.

Keywords: Optimal control, Robotics, Optimization algorithms
Abstract: Non-prehensile manipulation presents a paradigm for manipulating objects that aims to extend the versatility of robotic tasks beyond conventional grasping. Integrating motion primitives like pushing, tipping, rolling, throwing, and sliding into robots brings forth a set of challenges related to sensing, control, and planning. In particular, planning becomes notably intricate when dealing with densely arranged obstacles. This work focuses on the act of pushing an object through a series of obstacles within a cluttered environment. In such a setting, it may become necessary to change the direction of the pushing action in order to navigate through narrow passages. Incorporating these directional switches into the problem formulation introduces certain challenges and resolves the problem to a task and motion planning problem (TAMP). To that end, we introduce a novel optimization-based algorithm tailored to a pusher-slider system, capable of handling highly constrained regions. This algorithm automatically determines a switching sequence and the corresponding trajectories. We transcribe the trajectory optimization problem using B-splines and solve it iteratively, jointly optimizing switching and trajectory parameters to establish a feasible path through the environment. Based on iterative solutions, the complexity of the optimization problem is increased by adding directional switches. Our method's effectiveness is demonstrated through simulation experiments in cluttered environments.

Keywords: Agents networks, Modeling, Network analysis and control
Abstract: This paper investigates novel epidemic spreading problems under the influence of opinion evolution in social networks, where the opinions reflect the public health concerns toward the epidemic. A coupled bilayer network is proposed, where the epidemics propagate over several communities through a physical network layer while the opinions evolve over the same communities through a social network layer. Specifically, the epidemic spreading process is described by a susceptible-infected-vigilant (SIV) model, which introduces opinion-dependent epidemic vigilance state compared with classical epidemic models. Additionally, a polar opinion dynamics model is adopted on the social network, which incorporates the infection prevalence and human stubbornness into the opinion evolution. By introducing an opinion-dependent reproduction number, we provide the stability analysis of disease-free and endemic equilibria and derive sufficient conditions for their global asymptotic stability. Simulations are conducted to verify the theoretical results.

Keywords: Agents networks, Markov processes, Predictive control for linear systems
Abstract: In this paper, we consider a multi-agent system, where part of the agents interact with a system that can be externally controlled. Such a system can be thought as a super-agent or the environment the multi-agents operates in. We model the agents’ decision process as a Markov Chain and the externally controlled system as a linear dynamical system. We formulate the problem of controlling the probability that a set of agents make a specific decision as a Model Predictive Control problem. Such a control problem formulation is completed by a controllability analysis of the system. Simulation results from a small-scale example reveal the potential of the considered modeling and control framework for applications where the decisions of a set of agents are to be influenced to achieve a desired objective.

Keywords: Agents networks, Modeling
Abstract: This paper studies a mathematical model of opinion dynamics on social networks, which features continuous opinions and binary actions. The binary actions are a suitable quantization of the opinions, which evolve in continuous time. The model thus takes the form of a differential equation with discontinuous right-hand side: we explore the asymptotic behavior of its Caratheodory solutions, which turns out to be unexpectedly rich. By considering specific classes of graphs, namely lines and rings, we not only find attractive extended equilibria where the opinions are not in agreement, but also limit cycles and solutions that exhibit the Zeno phenomenon, whereby switching points accumulate in finite time.