Keywords: Traffic control, Transportation systems, Optimization
Abstract: The scientific, technological, social and economic impact of successful research in road traffic control is very significant, with immediate effects on safety, quality of life, environment, use of energy resources, and transportation costs. Yet, the development of effective methods and algorithms for road traffic management has to face notable methodological challenges. In addition, the type of traffic control strategies developed so far, the “classical approaches”, need now to be updated and adapted to consider the fast development in automotive technologies, traffic sensors, data processing, and communication. This lecture will address these aspects, starting from an overview of classical traffic control concepts to arrive at encompassing emerging research trends at different scales: from the microscopic scale of individual connected and autonomous vehicle control, to the macroscopic scale of road traffic control, also illustrating how the different scales can efficiently coexist in an advanced vehicular traffic control system. This will be done paying a particular attention to electric mobility, since it seems destined to become the dominant mobility paradigm in the next decades.

Keywords: Adaptive control, Optimal control, Uncertain systems
Abstract: We present a formulation for both implicit and explicit dual model predictive control for a steel recycling process. The process consists in the production of new steel by choosing a combination of several different steel scraps with unknown pollutant content. The pollutant content can only be measured after a scrap combination is molten, allowing for inference on the pollutants in the different scrap heaps. The production cost should be minimized while ensuring high quality of the product through constraining the maximum amount of pollutant. The dual control formulation allows to achieve the optimal explore-exploit trade-off between uncertainty reduction and cost minimization for the examined problem. Specifically, the dual effect is obtained by considering the dependence of the future pollutant uncertainties on the scrap selection in the predictions. The implicit formulation promotes uncertainty reduction indirectly via the impact of active constraints on the objective, while the explicit formulation adds a heuristic cost on uncertainty to encourage active exploration. We compare the formulations by numerical simulations of a simplified but representative industrial steel recycling process. The results demonstrate the superiority of the two dual formulations with respect to a robustified but non-dual formulation.

Keywords: Predictive control for nonlinear systems, Neural networks, Robotics
Abstract: Model predictive Path-Following Control (MPFC) is a viable option for motion systems in many application domains. However, despite considerable progress on tailored numerical methods for predictive control, the real-time implementation of predictive control and MPFC on small-scale autonomous platforms with low-cost embedded hardware remains challenging. While usual stabilizing MPC formulations lead to static feedback laws, the MPFC feedback turns out to be dynamic as the path parameter acts as an internal controller variable. In this paper, we leverage deep learning to implement predictive path-following control on microcontrollers. We show that deep neural networks can approximate the dynamic MPFC feedback law accurately. Moreover, we illustrate and tackle the challenges that arise if the target platform employs limited precision arithmetic. Specifically, we draw upon a post-stabilization with an additional feedback law to attenuate undesired quantization effects. Simulation examples underpin the efficacy of the proposed approach.

Keywords: Optimization algorithms, Power plants, Hybrid systems
Abstract: Unit Commitment (UC) problems are an essential set of problems in the power industry with applications in energy grid or heating systems management and control. The engineering goal is to balance the demand with the production of a network of production units, called generators, by providing a schedule and operating points for each generator cost-effectively while considering constraints. The constraints are caused by the dynamics of the system, the limits on the reserves, and possible robustness requirements. Due to the appearance of the on/off states from the generators, the resulting problems are NP-hard to solve. Thus, existing techniques to achieve a cost-efficient solution are computationally expensive. This paper proposes a dynamic programming-based heuristic to solve a UC problem. The heuristic focuses on finding a feasible and cost-effective solution for systems with a limited number of generators where a long time horizon is important. This method is compared to a Mixed Integer Linear Program (MILP) implementation for a micro-grid where it achieves a computation time that is an order of magnitude smaller than MILP programs for problems with a limited number of generators but a long time horizon.

Keywords: Predictive control for nonlinear systems, Adaptive control, Uncertain systems
Abstract: We propose an interaction-aware stochastic model predictive control (MPC) strategy for lane merging tasks in automated driving. The MPC strategy is integrated with an online learning framework, which models a given driver's cooperation level as an unknown parameter in a state-dependent probability distribution. The online learning framework adaptively estimates the surrounding vehicle’s cooperation level with the vehicle’s past state trajectory and combines this with a kinematic vehicle model to predict the distribution of a multimodal future state trajectory. Learning is conducted using logistic regression, enabling fast online computations. The multi-future prediction is used in the MPC algorithm to compute the optimal control input while satisfying safety constraints. We demonstrate our algorithm in an interactive lane changing scenario with drivers in different randomly selected cooperation levels.

Keywords: Predictive control for nonlinear systems, Constrained control, Stochastic control
Abstract: This paper compares novel methods to efficiently include input constraints using the nonlinear Model Predictive Path Integral (MPPI) approach. The MPPI algorithm solves stochastic optimal control problems and is based on sampled trajectories. MPPI results from the physical path integral framework. Sample-based algorithms are characterized by the fact that they can be computed in parallel and offer the possibility to handle discontinuous dynamics and cost functions. However, using standard MPPI the input costs in the Lagrange term have to be chosen quadratic. This fact is unfavorable for various real applications. Further, in standard nonlinear model predictive control (NMPC) approaches hard box constraints on the control input trajectory can be treated directly. In this contribution, novel architectures based on integrator action are compared. The investigated input constraint MPPI controllers were tested on an autonomous self-balancing vehicle. Therefore both, simulation and real-world experiments are presented. This paper addresses the question of how the MPPI algorithm can be further developed to consider input box constraints. Videos of the self-balancing vehicle are available at: https://tinyurl.com/mvn8j7vf

Keywords: Robust control, Autonomous robots, Predictive control for nonlinear systems
Abstract: This paper presents an implementation of robust model predictive control (MPC) for collision-free reference trajectory tracking for mobile robots. The presented approach considers the robot motion to be subject to process noise bounded by ellipsoidal sets. In order to efficiently handle the evolution of the disturbance ellipsoids within the MPC, the zero-order robust optimization (zoRO) scheme is applied. The idea is to fix the disturbance ellipsoids within one optimization iteration and solve the problem repeatedly with updated disturbance ellipsoid trajectories. The zero-order approach is suboptimal in general. However, we show that it does not impair convergence to the reference trajectory in the absence of obstacles. The experiments on an industrial mobile robot prototype demonstrate the performance of the controller.

Keywords: Adaptive control, Neural networks, Uncertain systems
Abstract: This study investigates the human-in-the-loop performance of a novel control framework, where a Long Short- Term Memory (LSTM) network augments an adaptive neural network (ANN) controller. The method drastically improves the transient response compared to conventional approaches, especially in the presence of significant and rapid changes in the uncertainties. LSTM network, which uses the knowledge of input sequence dependencies, predicts and compensates for the deviation of the ANN controller from its ideal behavior. Although this control framework is shown to provide improved transients, its interactions with a human operator need to be analyzed to ensure a safe operation. In this study, first, a human pilot model is used to investigate the overall system’s behavior and analyze the controller’s performance for a reference tracking task. Then, human-in-the-loop experiments are conducted to analyze how the system responds in the presence of a real human operator in the loop.

Keywords: Iterative learning control, Neural networks, Computational methods
Abstract: We consider the problem of sequential parameter estimation of a nonlinear function under the Bayesian setting. The designer can choose inputs for a sequence of experiments to obtain an accurate estimate of the system parameters based on observed outputs, while complying with a constraint on the expected outputs of the system. We quantify the accuracy of the obtained estimate in terms of the ell_2 norm. We propose to solve the problem by casting it as the problem of minimizing the Bayesian Mean Square Error (BMSE) of the parameter estimate subject to a constraint on the expected deviation of the output from the desired target value. We develop a greedy policy to solve the problem in the sequential setting, and we characterize the solution structure based on analytical results for the Gaussian case. For a computationally tractable update of the posterior, we propose the use of a surrogate model combined with approximate Bayesian computation. We evaluate the proposed approach on the use case of smart road compaction, where the goal is to estimate asphalt parameters while reaching the desired compaction level, by choosing the value of the loading pressure. Simulation results on a synthetic road compaction dataset show the efficacy of the proposed solution scheme in both parameter estimation and effective compaction of the road.

Keywords: Medical signal processing, Neural networks
Abstract: The objective of this paper is to investigate a new technique for skin lesion classification based on discrete wavelet transforms and convolutional networks. More precisely, for preprocessing the images, two 2D family wavelets and Low-Low (LL), High-High (HH) bands were used to generate subimages for each skin lesion image as melanoma and normal nevi. On each subimage datasets of nevi and melanoma, five convolutional neural networks (CNNs) were tested. The experiments were conducted on the public Med-node dataset which contains 170 images (70 melanoma and 100 nevi cases). The accuracy, precision, recall, and F1-score metrics were extracted from confusion matrix for each test performed on the proposed CNN architecture. All metrics show that LL subimages are recommended to be used in classifying melanoma vs. nevi.

Keywords: Uncertain systems, Adaptive control, Neural networks
Abstract: We develop a learning-based algorithm for the control of autonomous systems governed by unknown, nonlinear dynamics toward trajectory tracking. Most existing algorithms either assume certain parametric forms for the unknown dynamic terms or resort to unnecessarily large control inputs in order to provide theoretical guarantees. The proposed algorithm addresses these drawbacks by integrating neural-network-based learning with adaptive control. More specifically, the algorithm learns a controller, represented as a neural network, using training data that correspond to a collection of system parameters and reference trajectories. These parameters and trajectories are derived by varying the nominal parameters and the reference trajectory, respectively. It then incorporates this neural network into an online closed-form adaptive control law in such a way that the closed-loop system tracks the reference trajectory. The proposed algorithm does not use any a priori information on the unknown dynamic terms or any approximation schemes. Comparative computer simulations demonstrate the effectiveness of the proposed algorithm.

Keywords: Iterative learning control, Neural networks, Aerospace
Abstract: This paper proposes a novel dynamic programming algorithm for nonlinear system optimal control problem, namely Incremental Generalized Policy Iteration (IGPI). The proposed IGPI algorithm combines the advantages of Incremental Control(IC) and Generalized Policy Iteration(GPI). Incremental control can deal with system nonlinearity and uncertainty without knowing the nonlinear system information, GPI can learn an optimal control law for dynamical systems. Based on the proposed IGPI algorithm, a data-driven adaptive attitude controller is designed for a spacecraft with sloshing liquid fuel. Simulation results demonstrate the effectiveness of the spacecraft attitude controller.

Keywords: Neural networks, Predictive control for nonlinear systems, Process control
Abstract: We present the design, training, and implementation of a nonlinear autoregressive neural network for the control of a multi-input, multi-output hydraulic plant. The network mimics the optimal control signals of a nonlinear model predictive controller and is implemented on a low-level microcontroller. While trained with simulation data only, experiments on the real plant show that not only the setpoint tracking, but to some degree also the constraint satisfaction and unmeasured disturbance rejection are adapted by the neural network. In contrast to the optimization-based predictive controller, the neural network easily runs on an ESP32 microcontroller and Micropython with guaranteed evaluation time and still achieves similar control performance as the predictive controller.

Keywords: UAV's, Predictive control for nonlinear systems, Feedback linearization
Abstract: Differential flatness has often been used to provide diffeomorphic transformations of non-linear dynamics, with the goal of reaching a linear controllable system with endogenous dynamic feedback. This greatly simplifies the control synthesis step since in the flat output space, the dynamic appears in canonical form (as a chain of integrators). The caveat is that mapping constraints from the original to the flat output space often leads to nonlinear constraints. In particular, the alteration of the feasible input set greatly hinders the subsequent calculations. In this paper, we particularize the problem for the case of the quadcopter dynamics and investigate the deformed, via the flat transformation, input constraint set. An optimization-based procedure will achieve a non-conservative, linear, inner-approximation of the non-convex, flat-output derived, input constraint set. Consequently, a receding horizon problem (linear in the flat output space) is easily solved and, via the inverse flat mapping, provides a feasible input to the original, nonlinear, quadcopter dynamics. Experimental validation and comparisons confirm the benefits of the proposed approach and show promise for other class of flat systems.

Keywords: Autonomous systems, UAV's, Optimization
Abstract: The paper extends previous work on trajectory generation for UAV (Unmanned Aerial Vehicles) using B-spline curves to parameterize an associated flat output. Typical constraints and costs (such as those involving input bounds and trajectory length) lead to nonlinear formulations in terms of the control points weighting the B-spline curve. This complexity adversely affects the computation time and conservatism of the result. To mitigate these effects we use the Schoenberg operator to provide a quasi-interpolant of the original nonlinear functions. These improvements come at the price of an approximation error which requires a tightening of the original constraints (either from a theoretical bound or via an iterative procedure). The obtained results are exemplified over a fixed wing UAV model and they can be applied for any optimization-based trajectory planning problem.

Keywords: Robotics, Predictive control for nonlinear systems, Autonomous robots
Abstract: This paper evaluates experimentally a novel strategy for solving a variant of the differential game of target defense in the presence of obstacles. The state-of-the-art approaches mostly employ an offline optimization strategy that is only applicable to holonomic systems. This paper presents an online optimization technique, by designing a trade-off parameter that integrates game theory with the model predictive control which allows a nonholonomic defender to intercept an attacker while simultaneously defending a specific target. Several indoor laboratory experiments validate the performance of the proposed approach and compared with a standard model predictive control approach.

Keywords: UAV's, Predictive control for nonlinear systems, Robotics
Abstract: In this paper we relax the standard NMPC (Nonlinear Model Predictive Control) through a variable terminal set construction for stabilizing a multicopter system. This contribution allows us to increase the feasible domain and/or reduce the required prediction horizon length. Furthermore, to reduce the complexity of the computations we use zonotopic sets which prove instrumental due to their efficient representation. The theoretical results are validated in simulation and experiment for the stabilisation along a trajectory of a nano-multicopter's translational dynamics.

Keywords: V&V of control algorithms, Machine learning, Computational methods
Abstract: In this paper, a methodology for estimating the region of attraction of stable equilibrium point using binary Gaussian process classification is proposed. Interests behind this method stem from the fact that the points in the state space can be classified either in the region of attraction or not. Using Gaussian process classification gives the predictive region of attraction and a minimum confidence level associated with the estimate. Moreover, the active learning scheme helps to update the Gaussian process classification model and make a better prediction by selecting informative observations, sequentially. The methodology is applied to several examples to illustrate effectiveness of this approach.

Keywords: UAV's, Cooperative control, Servo control
Abstract: Unmanned aerial vehicles (UAVs) are becoming common technology used for different purposes. Most of the applications constitute monitoring and surveillance. In addition to being passive actors in search and rescue missions, UAVs take on more active roles in the form of aerial manipulators capable of physical interaction with the environment. In this paper, we present the collaboration approach between UAV and robot manipulator, where the robot manipulator plays the role of visual observer. we also present a UAV control based on visual servoing, a state machine for suction-based object grasping in a GNSS-denied scenario and study effect of the different message drop probabilities on the system performance. The presented scenario is one of the tasks of MBZIRC competition. Results are presented in official contest simulator, Ignition Gazebo.

Keywords: Adaptive systems, Fault detection and identification, Intelligent systems
Abstract: In this paper, a novel Adaptive Constrained Clustering algorithm is defined to support real time fault detection of an industrial machine, by clustering the incoming monitoring data into two clusters over the time, representing the nominal and non-nominal work conditions, respectively. To this aim, the proposed algorithm relies on a two-stage procedure: microclustering and constrained macro-clustering. The former stage is responsible for grouping the batches of work-cycle data into micro-clusters, while data stream continuously arrives from the data acquisition system. Then, after condensing the microclusters into vectors of cluster features, and leveraging on additional knowledge on the nominal working condition (i.e., clustering constraints on some samples), the second stage aims at offline grouping the micro-clusters features into macro-clusters. Experimental results on a real-world industrial case study show that the proposed real time framework achieves the same results as offline baseline methods, thus improving the responsiveness and the processing speed with respect to the static approach.

Keywords: Fault diagnosis, Linear time-varying systems
Abstract: This paper presents a data-driven fault detection method combining structural analysis (SA) and machine learning data-driven algorithms. Given a graphic (or textual) system description and the available measured inputs/outputs time, the strucure of analytical redundancy relations (ARRs) between some inputs and outputs can be determined with the aid of system SA. Then, using a machine learning data-driven approach (ANFIS) applied to historical data, an analytical expressions can be obtained between inputs and outputs. Thereby, instead of finding ARRs from physical mathematical model, combining SA and ANFIS using historical data, a set of data-driven ARRs can be obtained and used to implement a diagnosis system. Once the ANFIS model has been identified, it is reformulated in linear parameter varying (LPV) form. Then, a fault detection scheme based on a LPV Kalman filter and pole placement method is developed. A well-known case study based on the fourt-tank system is used for illustrative purposes.

Keywords: Fault tolerant systems, Fault detection and identification, Observers for linear systems
Abstract: This paper considers the problem of set-based state estimation for linear time-invariant (LTI) systems under time-varying sensor attacks. Provided that the LTI system is stable and observable via every single sensor and that at least one sensor is uncompromised, we guarantee that the true state is always contained in the estimated set. We use zonotopes to represent these sets for computational efficiency. However, we show that intelligently designed stealthy attacks may cause exponential growth in the algorithm's worst-case complexity. We present several strategies to handle this complexity issue and illustrate our resilient zonotope-based state estimation algorithm on a rotating target system.

Keywords: Fault detection and identification, Fault diagnosis
Abstract: This paper solves the problems of the additive system fault detection based on the asymptotic reduced-order observers, which can estimate the system states. To construct the fault residual filter when additive faults may occur in the system, an adaptation of this principle is proposed. The constraints for the existence of asymptotic reduced-order observers are newly given and the necessary structure of the fault residual filter is introduced. A numerical example is given to illustrate the design and the effectiveness of the proposed method.

Keywords: Fault diagnosis, Machine learning, Transportation systems
Abstract: This study concerns the development of fault diagnosis methods for railway switches based on supervised machine learning. The lack of data led us to build a digital twin that allows us to build the dataset necessary to train the model. The data measured on the switches correspond to time series. We are therefore interested in supervised learning methods based on time series. Our study shows that the STSF model is the best suited for the classification of switch faults. The study then focused on characterizing the robustness of the model with respect to noises that can disturb the measurements. It also showed that the current I and the angular velocity ω are the most relevant data for learning. The results give the main conditions to ensure an optimal implementation on real systems by transfer learning.

Keywords: Fault diagnosis, Switched systems, Process control
Abstract: Fault diagnosis is essential in managing abnormal changes in system characteristics which affect its functioning and control abilities. The manuscript provides a set-based fault diagnosis for a fluidic benchmark. Invariant sets are employed to characterize the steady-state behavior of the open-loop stable dynamics for sensor faults. It is shown that the set-based fault diagnosis theory integrates this model and is able to handle efficiently persistent fault events.

Keywords: Maritime, Autonomous systems, Predictive control for nonlinear systems
Abstract: The automation of inland waterway vessels for the transportation of goods offers great potential to increase safety and reduce the environmental impact of the transport sector but is challenging due to the large vessels and narrow waterways. Accurate path tracking is necessary for narrow rivers and canals. In addition, inland vessels possess high inertia and often are under-actuated during en-route traveling. Therefore, foresighted driving is necessary to keep an inland vessel within the fairway and avoid collisions with obstacles, such as encountered vessels. In this paper, a model predictive path following control is presented for purely rudder-steered inland vessels to enable safe driving through rivers and canals. Path following is achieved by minimizing the cross-track error between the bow of the vessel and the reference path in the cost function. A constructive solid geometry (CSG) approach ensures that the vessel always remains in the fairway. For collision avoidance with an obstacle, a dual formulation of the signed distance is used, which considers the convex geometries of the vessel and the obstacle. It is shown by means of Matlab/Simulink simulations on a section of the Dortmund- Ems Canal that the developed controller is capable of steering a 105m long vessel safely through the canal section.

Keywords: Maritime, Autonomous systems
Abstract: This article tests the method for inferring and modeling ship intentions presented in [1] on real ship encounters gathered through the automatic identification system (AIS) that all larger ships are required to use. Empirical distributions on how early ships tend to perform avoidance maneuvers and how close they tend to come are evaluated. These are used by the intention model to identify when a ship's behavior is outside normal behavior. Running the intention model on the historical ship encounters demonstrates that the intention model is able to correctly infer the intentions of ships in real collision encounters. The model is able to distinguish between different types of incompliant behavior such as a ship not giving way when it should, the wrong ship giving way, or a ship giving way in the wrong direction. Some improvement potentials are identified, mainly with respect to understanding when the situation starts.

Keywords: Maritime, Constrained control, Output feedback
Abstract: Maritime motion control systems traditionally employ proportional-integral-derivative (PID) feedback control combined with a model-based feedforward structure for heading control. However, such control designs often suffer from the widely accepted limitation that the transient response from disturbances or significant measurement steps will not comply with operational motion constraints. This can pose a risk to onboard passengers and cargo, as the control law can impose motions that violate safety regulations. To address this limitation, we present a novel and simple control design that improves constraint handling while providing feasible rejection of external disturbances. Our design is based on a cascaded structure consisting of an outer-loop heading control law and an inner-loop rate-of-turn control law. The main contribution is the nonlinear feedback design of the outer-loop control law, which uses dynamic augmentation of the heading kinematics and nested saturation functions applied to the resulting second-order kinematics. We prove that the design is input-to-state stable with respect to the rate-of-turn error. The design has similar complexity as traditional designs with respect to implementation and tuning. The feasibility of the design is showcased with a simulation case study. The results demonstrate that the control design effectively handles operational constraints while maintaining good performance. The design has significant potential for real-world application in maritime motion control systems, as it provides a simple yet effective way to ensure compliance with operational constraints on heading rate and acceleration.

Keywords: Maritime, Transportation systems, Modeling
Abstract: In this paper we propose a novel methodology for finding safe time-space corridors (tubes) that enable navigation through natural waterways of time-varying depth while robustly minimizing the probabilities of suffering a grounding or crashing accident, satisfying the constraints imposed by local authorities, and arriving to the final destination before the time limits imposed by contract between the local authorities and the charterer or manager of the vessel. The objective is that these tubes can later be employed by different scheduling, planing or control algorithms with the objective of optimizing navigation in the waterway. The proposed approach is applied to the case of the Guadalquivir river in the south of Spain where navigation is conditioned by the irregular bathymetric profile of the river and by the effect of the tide.

Keywords: Optimal control, UAV's, Maritime
Abstract: Over the past few years, a plethora of advancements in Unmanned Areal Vehicle (UAV) technology has paved the way for UAV-based Search and Rescue (SAR) operations with transformative impact to the outcome of critical life-saving missions. This paper dives into the challenging task of multiple castaway tracking using an autonomous UAV agent. Leveraging on the computing power of the modern embedded devices, we propose a Model Predictive Control (MPC) framework for tracking multiple castaways assumed to drift afloat in the aftermath of a maritime accident. We consider a stationary radar sensor that is responsible for signaling the search mission by providing noisy measurements of each castaway's initial state. The UAV agent aims at detecting and tracking the moving targets with its equipped onboard camera sensor that has limited sensing range. In this work, we also experimentally determine the probability of target detection from real-world data by training and evaluating various Convolutional Neural Networks (CNNs). Extensive qualitative and quantitative evaluations demonstrate the performance of the proposed approach.

Keywords: Predictive control for nonlinear systems, Optimal control, Maritime
Abstract: This work presents and implements a decoupled Thrust Allocation (TA) algorithm for overactuated inland surface vessels. The utilised control strategy is Nonlinear Model Predictive Control (NMPC), which solves an Optimal Control Problem (OCP) over a control horizon. The controller optimises for minimum energy consumption, subject to the desired responsiveness. We describe the established NMPC formulation as a Nonlinear Programming Problem (NLP) employing the multiple shooting method. Here we use a variety of expressions for nonlinear constraints and saturation models of the propulsion system. Finally, we detail the implementation of the NLP. The resulting implementation has been evaluated on computational load and convergence, embedded on a scale model, during an on-land stationary test.

Keywords: Robotics, Computational methods, Autonomous systems
Abstract: This paper introduces a novel and computationally efficient policy representation, termed a banded controller, for Partially Observable Markov Decision Processes (POMDPs). The structure of a banded controller is obtained by restricting the number of successor nodes for each node in a finite state controller (FSC) policy representation; this is formally defined as the restriction of the controller's node transition matrices to the space of banded matrices. A gradient ascent based algorithm which leverages banded matrices is presented and we show that the policy structure results in a computational structure that can be exploited when performing policy evaluation. We then show that policy evaluation is asymptotically superior to a general FSC and that the degrees of freedom can be reduced while maintaining a large amount of expressivity in the policy. Specifically, we show that banded controller policy representations are equivalent to any FSC policy which is permutation similar to a banded controller. Meaning that banded controllers are computationally efficient policy representations for a class of FSC policies. Lastly, experiments are conducted which show that banded controllers outperform state-of-the-art FSC algorithms on many of the standard benchmark problems.

Keywords: Robotics, Algebraic/geometric methods, Mechatronics
Abstract: In this paper, a novel approach to visual servo control robotic systems is proposed. It is focused on developing a solution using 3D point features without recovering the rigid object's pose. Pose-free motion is achieved using motion parameterization techniques based on dual numbers and dual vectors. Considering an imposed velocity field over the motion of the 3D point features ensemble, this work proposes a close-form solution to a visual servoing problem. The solution provides stable motion control while preserving the image features in the field of view. However, when some point features leave the field of view, their contribution to the control law is dropped without losing stability. The proposed solution is easy to tune and implement. Various scenarios are used in simulations and real experiments to show how the proposed solution overcomes classic servoing problems.

Keywords: Robotics, Coverage control, Optimization
Abstract: This paper considers a multi-sensor service matching deployment problem over a set of discrete target points that populate a finite flat surface. The service can be event detection among targets using a vision sensor or an acoustic receiver, video surveillance for target monitoring, or providing wireless coverage to the targets. The quality-of-service (QoS) of the sensors is spatially nonuniform and can be anisotropic. The sensors are heterogeneous in the sense that their QoS distribution over their sensing footprint is not the same. The objective is to determine the sensor's best deployment position and orientation such that the collective multi-sensor QoS distribution matches the spread of the targets in the environment as closely as possible. To solve this problem, we propose a two-stage deployment strategy. First, we partition the environment using the computationally efficient K-means clustering algorithm. Then, we sample points from the QoS distribution over the sensing footprint. Then, for each sensor-cluster pair, we use an iterative closest-point approach inspired by the point cloud registration algorithms used in computer vision to determine the best deployment position and orientation for the sensor. Finally, we use a linear assignment problem framework to assign the clusters to the sensors. Numerical examples demonstrate our results.

Keywords: Robotics, Intelligent systems, Autonomous systems
Abstract: Robots have been steadily increasing their presence in our daily lives, where they can work along with humans to provide assistance in various tasks on industry floors, in offices, and in homes. Automated assembly is one of the key applications of robots, and the next generation assembly systems could become much more efficient by creating collaborative human-robot systems. However, although collaborative robots have been around for decades, their application in truly collaborative systems has been limited. This is because a truly collaborative human-robot system needs to adjust its operation with respect to the uncertainty and imprecision in human actions, ensure safety during interaction, etc. In this paper, we present a system for human-robot collaborative assembly using learning from demonstration and pose estimation, so that the robot can adapt to the uncertainty caused by the operation of humans. Learning from demonstration is used to generate motion trajectories for the robot based on the pose estimate of different goal locations from a deep learning-based vision system. The proposed system is demonstrated using a physical 6 DoF manipulator in a collaborative human-robot assembly scenario. We show successful generalization of the system's operation to changes in the initial and final goal locations through various experiments.

Keywords: Robotics, Machine learning, Uncertain systems
Abstract: Insertion operations are a critical element of most robotic assembly operation, and peg-in-hole (PiH) insertion is one of the most widely studied tasks in the industrial and academic manipulation communities. PiH insertion is in fact an entire class of problems, where the complexity of the problem can depend on the type of misalignment and contact formation during an insertion attempt. In this paper, we present the design and analysis of adaptive compliance controllers which can be used in insertion-type assembly tasks, including learning-based compliance controllers which can be used for insertion problems in the presence of uncertainty in the goal location during robotic assembly. We first present the design of compliance controllers which can ensure safe operation of the robot by limiting experienced contact forces during contact formation. Consequently, we present analysis of the force signature obtained during the contact formation to learn the corrective action needed to perform insertion. Finally, we use the proposed compliance controllers and learned models to design a policy that can successfully perform insertion in novel test conditions with almost perfect success rate. We validate the proposed approach on a physical robotic test-bed using a 6-DoF manipulator arm.

Keywords: Robotics, Modeling
Abstract: In this paper, we show that the asymmetrical, and changing, efficiency of a robot joint has significant effect on the accuracy of sensorless force estimation methods. Our work is based on a high-fidelity strain wave gear model that includes gear meshing friction forces. The meshing friction cause the efficiency of the gear to depend on the load and on whether the joint is in forward drive or backward drive. The changing efficiency is not captured by standard robot dynamic models which assume rigid joints nor by most other high-fidelity models. We use the presented gear model for sensorless force estimation using a generalized momentum observer that allows the estimation of externally applied forces on the links of the robot. The external force estimation is pivotal for the control applied during kinesthetic teaching of collaborative robots.

Keywords: Stability of hybrid systems, Stability of nonlinear systems, Lyapunov methods
Abstract: We investigate the instability property of finite dimensional impulsive systems by means of Lyapunov-like tools. Auxiliary functions of Chetaev type are used. Sufficient conditions restricting the time intervals between impulsive actions guaranteeing the instability are provided. These conditions cover in particular the case where both discrete and continuous dynamics are stable. By means of an example we demonstrate that even in this case the instability can take place.

Keywords: Stability of linear systems, LMI's/BMI's/SOS's, Linear systems
Abstract: This manuscript suggests generalized stability results of time-varying delay systems by utilizing a sufficient and necessary condition on the high-order polynomial inequality and a generalized integral inequality. Firstly, the Lyapunov-Krasovskii functionals are established in a generalized form to the bounding order. Secondly, the upper bound of a single integral in quadratic matrix form stacked with two state information is estimated through the generalized integral inequality which can provide an estimate generalized in the bounding order. Finally, the negative-definiteness conditions on a high-order matrix-valued polynomial, obtained from the stability analysis results, are converted to the linear matrix inequality conditions by applying the lemma of necessary and sufficient conditions on high-order polynomial inequality. Two numerical examples clearly show that the generalized stability results outperform the stability results of other conventional papers.

Keywords: Stability of nonlinear systems, Nonlinear system theory, Lyapunov methods
Abstract: In the present work, we provide bifurcation analysis of a second-order type 1 phase-locked loop (PLL) with a lead-lag loop filter and a piecewise-linear phase detector characteristic.

Keywords: Uncertain systems, Stability of nonlinear systems
Abstract: In this paper, a first step towards achieving prescribed performance attributes on the output tracking error for uncertain continuous-time nonlinear systems in the presence of finite-time intervals of state measurement losses, is made. The proposed control strategy guarantees that the output tracking error is confined in a pre-specified desired region, in fixed-time no greater than a pre-defined constant, which can be selected arbitrarily small, provided that in the absence of state-loss phenomenon, a minimum time interval of successfully received measurement is guaranteed. Further, we prove that all signals in the closed-loop remain bounded. The theoretical findings are validated by simulation studies.

Keywords: Stability of hybrid systems, Stability of nonlinear systems, Lyapunov methods
Abstract: This article proposes two approaches to study the asymptotic stability of a non-linear coupled impulsive system with time-invariant subsystems. The first of them involves the use of the weighted maximum of components of a Lyapunov vector function, and the second one involves the use of Lyapunov vector function combine with the comparison method.

Keywords: Robust control, MEMS, Passivity Based Control

Keywords: H2/H-infinity methods, Robust control, Linear parameter-varying systems
Abstract: Laser Interferometer Gravitational wave Observatory (LIGO) is a gravitational wave antenna used to detect the infinitesimal contraction and expansion of space by a passing gravitational wave. LIGO comprises several subsystems which isolate the optics from seismic noise. Of these, we focus on the quadruple pendulum suspension system and attempt to design a stabilizing controller for a simpler version of the system, the two wire simple pendulum. A nonlinear model of the two wire simple pendulum based on the Euler-Lagrange theory is derived. This is linearised about an equilibrium and a stabilising controller using a robust control paradigm called H∞ ynthesis, is explored as a candidate design paradigm for the two wire simple pendulum. In particular, in this broad framework of H∞ synthesis we adopt a technique called coprime factorisation. Different types of uncertainties like parametric and nonlinear uncertainties have also been considered and an upper bound on the nonlinear uncertainties in the system is determined.

Keywords: Robust control, Uncertain systems, Linear systems
Abstract: In this work, we propose a new approach for the robust static output feedback (SOF) stabilization of linear time-invariant (LTI) systems with transient performance. Recently published non-convex necessary and sufficient conditions for the SOF stabilizability of LTI systems are leveraged to obtain sufficient convex conditions which have the benefit of being numerically tractable. Here, we propose an adaption to a recently developed iterative algorithm to obtain sufficient linear matrix inequalities (LMIs) conditions that are conveniently solved using SDP tools. Numerical examples highlight that the proposed strategy leads to better results in terms of a numerical comparison with the most recent developments in the literature.

Keywords: Robust control, Uncertain systems, Mechatronics
Abstract: This paper proposes a comparative study of uncertainty modelling approaches for inverted-pendulum type mechanical systems with input-output derivative behaviour to be directly used for robust controller synthesis. Besides three approaches from the literature to obtain the mathematical model of the parametric uncertainties, we propose an additional nonlinear optimization-based approach, which manages to maintain the reduced-order structure of the convex optimization fit, but with better performance and robustness attainable through less conservativeness of the obtained model. A numerical case study is presented for a set of parameter values and tolerances with a closed-loop shaping design procedure.

Keywords: Model/Controller reduction, Robust control, Uncertain systems
Abstract: The fractional-order (FO) element has been recently integrated into the generalized Robust Control Framework using the Oustaloup method. As such, the parametric uncertainties from fractional-order interval plants can now be treated using the structured singular value framework. Moreover, the resulting infinite impulse response approximation manages to satisfy the robust stability and the robust performance criteria according to the given uncertainty block. However, the recommended approximation order for each fractional-order element is equal to the number of decades of the frequency range where the approximation is valid, which can lead to a high-order controller representation. The aim of this paper is to present two methods for order reduction without losing the robustness properties, followed by an optimization problem whose solution is a low-order approximation of a fractional-order controller which maintains the robust stability and robust performance as well. A finite commensurate order interval plant has been used as a numerical example to illustrate the effectiveness of the proposed method.

Keywords: Predictive control for linear systems, Constrained control, Robust control
Abstract: This paper makes comparisons between a reference governor scheme and a tube-based MPC algorithm for a simple tracking problem with output disturbances. The key aim is to consider the extent to which a tube based approach is merited in this scenario, or whether the same feasibility assurances and good performance can be obtained with a far simpler approach. The main features, properties, and general setup of both methods are described and compared. The tracking performance of both algorithms are analysed.

Keywords: Robust control, Traffic control, Uncertain systems
Abstract: In this paper, we study a safe control design for dynamical systems in the presence of uncertainty in a dynamical environment. The worst-case error approach is considered to formulate robust Control Barrier Functions (CBFs) in an optimization-based control synthesis framework. It is first shown that environmentally robust CBF formulations result in second-order cone programs (SOCPs). Then, a novel scheme is presented to formulate robust CBFs which takes the nominally safe control as its desired control input in optimization-based control design and then tries to minimally modify it whenever the robust CBF constraint is violated. This proposed scheme leads to quadratic programs (QPs) which can be easily solved. Finally, the effectiveness of the proposed approach is demonstrated on an adaptive cruise control example.

Keywords: Large-scale systems, Chemical process control, Optimization
Abstract: TBD

Keywords: Large-scale systems, Chemical process control, Optimization
Abstract: TBD

Keywords: Large-scale systems, Chemical process control, Optimization
Abstract: TBD

Keywords: Large-scale systems, Chemical process control, Optimization
Abstract: TBD

Keywords: Large-scale systems, Chemical process control, Optimization
Abstract: TBD

Keywords: Optimal control, Optimization algorithms, Predictive control for nonlinear systems
Abstract: We revisit three classical numerical methods for solving unconstrained optimal control problems – differential dynamic programming, direct single shooting, and direct multiple shooting – and examine their local convergence behaviour. In particular, we show that all three methods converge with the same linear rate if a Gauss-Newton (GN) – or more general a Generalized Gauss-Newton (GGN) – Hessian approximation is used, which is the case in widely used implementations such as iLQR.

Keywords: Optimization algorithms, Optimization, Stochastic filtering
Abstract: We look at a stochastic time-varying optimization problem and we formulate online algorithms to find and track its optimizers in expectation. The algorithms are derived from the intuition that standard prediction and correction steps can be seen as a dynamical system and a measurement equation, respectively, yielding the notion of filter design. The optimization algorithms are then based on an extended Kalman filter in the unconstrained case, and on a linear matrix inequality condition in the constrained case. Some special cases and variations are discussed, and supporting numerical results are presented from real data sets in ride-hailing scenarios. The results are encouraging, especially when predictions are accurate, a case which is often encountered in practice when historical data is abundant.

Keywords: Optimization, Optimal control, Optimization algorithms
Abstract: In this paper, techniques for reducing the number of optimization variables for quadratic programs arising in model predictive control are reviewed. Focusing on optimization with a first-order method, numerical properties such as the condition number of the Hessian are evaluated and suboptimality due to early termination of the optimization algorithm is investigated. The state elimination condensing approach and a recently proposed numerically robust approach based on the QR factorization are compared to the existing methods of prestabilizing the prediction and preconditioning for reducing the Hessian condition number for linear MPC. For the application example of controlling a fuel cell system, the worst-case turnaround time of the control algorithm can be reduced by more than 30% compared to standard state elimination algorithms. Thus the controller is shown to be real-time feasible using the QR factorization or additional preconditioning.

Keywords: Optimization algorithms, Optimization, Computational methods
Abstract: In this paper we consider convex composite optimization problems, where first term is smooth, while the second term is proximal easy but nonseparable (possibly nonsmooth). For this problem we adapt the accelerated proximal coordinate descent algorithm from [6], initially developed for convex composite problems having the second term separable. We study convergence to a coordinate-wise minimizer point, and derive convergence rate in expected function values which depends on two terms. The first term, coincides with the usual constant appearing in the rate of accelerated gradient type methods, while the second one, measures the nonseparability of the second term in the objective along the iterates. We conjecture that the second term, is bounded as this is what we observe in all our numerical simulations and that coordinate descent can be accelerated.

Keywords: Optimization algorithms, Optimization, Lyapunov methods
Abstract: This paper proposes a novel solution for the distributed unconstrained optimization problem where the total cost is the summation of time-varying local cost functions of a group networked agents. The objective is to track the optimal trajectory that minimizes the total cost at each time instant. Our approach consists of a two-stage dynamics, where the first one samples the first and second derivatives of the local costs periodically to construct an estimate of the descent direction towards the optimal trajectory, and the second one uses this estimate and a consensus term to drive local states towards the time-varying solution while reaching consensus. The first part is carried out by a weighted average consensus algorithm in the discrete-time framework and the second part is performed with a continuous-time dynamics. Using the Lyapunov stability analysis, an upper bound on the gradient of the total cost is obtained which is asymptotically reached. This bound is characterized by the properties of the local costs. To demonstrate the performance of the proposed method, a numerical example is conducted that studies tuning the algorithm's parameters and their effects on the convergence of local states to the optimal trajectory.

Keywords: Optimal control, UAV's, Optimization
Abstract: This work aims to develop an agile trajectory-tracking controller for aerial vehicles based on the genetic optimization algorithm. The main goal is to minimize the error between the reference position and velocity from the trajectory and the aerial vehicle position and velocity at time t. We used an aerial vehicle dynamic model with PI for the attitude controller and PID for the attitude rate controller. The cross entropy-based optimization algorithm is utilized for generating pitch and roll references to track a desired agile trajectory. Simulation results are conducted to compare our proposed solution with deep reinforcement learning-based trajectory tracking controllers. Our approach has been applied to the Crazyflie quadcopter to track the given agile trajectories with acceleration up to boldsymbol{6.2 m/s^2}, while keeping the root-mean-square tracking error down to 3.6 cm.

Keywords: Optimization, Adaptive control, Machine learning
Abstract: Entropy regularization is an efficient technique for encouraging exploration and preventing a premature convergence of (vanilla) policy gradient methods in reinforcement learning (RL). However, the theoretical understanding of entropy-regularized RL algorithms has been limited by the assumption of exact gradient oracles. To go beyond this limitation, we study the convergence of stochastic soft-max vanilla policy gradient with entropy regularization and prove how to utilize the curvature information around the optimal policy to guarantee that the action probabilities will still remain uniformly bounded with high probability. Moreover, we develop the “last iterate” convergence and sample complexity result for the proposed algorithm given a good initialization.

Keywords: Optimization algorithms, Optimal control
Abstract: We address black-box convex optimization problems, where the objective and constraint functions are not explicitly known but can be sampled within the feasible set. The challenge is thus to generate a sequence of feasible points converging towards an optimal solution. By leveraging the knowledge of the smoothness properties of the objective and constraint functions, we propose a novel zeroth-order method, SZO-QQ, that iteratively computes quadratic approximations of the constraint functions, constructs local feasible sets and optimizes over them. We prove convergence of the sequence of the objective values generated at each iteration to the minimum. Through experiments, we show that our method can achieve faster convergence compared with state-of-the-art zeroth-order approaches to convex optimization.

Keywords: Lyapunov methods, Machine learning, Constrained control
Abstract: It has been shown that optimizing quadratic costs while stabilizing affine control systems to desired (sets of) states subject to state and control constraints can be reduced to a sequence of Quadratic Programs (QPs) by using Control Barrier Functions (CBFs) and Control Lyapunov Functions (CLFs). In this paper, we employ machine learning techniques to ensure the feasibility of these QPs, which is a challenging problem, especially for high relative degree constraints where High Order CBFs (HOCBFs) are required. To this end, we propose a sampling-based learning approach to learn a new feasibility constraint for CBFs; this constraint is then enforced by another HOCBF added to the QPs. The accuracy of the learned feasibility constraint is recursively improved by a recurrent training algorithm. We demonstrate the advantages of the proposed learning approach to constrained optimal control problems with specific focus on a robot control problem and on autonomous driving in an unknown environment.

Keywords: Machine learning, Markov processes, Safety critical systems
Abstract: In this paper, we aim to study safety specifications for a Markov decision process with stochastic stopping time in an almost model-free setting. Our approach involves characterizing a proxy set of the states that are near in a probabilistic sense to the set of unsafe states - forbidden set. We also provide results that relate safety function with reinforcement learning. Consequently, we develop an algorithm based on the temporal difference method to compute the safety function. Finally, we provide simulation results that demonstrate our work in a simple example.

Keywords: Predictive control for nonlinear systems, Constrained control, Safety critical systems
Abstract: A predictive control barrier function (PCBF) based safety filter is a modular framework to verify safety of a control input by predicting a future trajectory. The approach relies on the solution of two optimization problems, first computing the minimal state constraint violation given the current state in the form of slacks on the constraint, and then computing the minimal deviation from a proposed input given the previously computed minimal slacks. This paper presents an approximation procedure that uses a neural network to approximate the optimal value function of the first optimization problem, which defines a control barrier function (CBF). By including this explicit approximation in a CBF-based safety filter formulation, the online computation becomes independent of the prediction horizon. It is shown that this approximation guarantees convergence to a neighborhood of the feasible set of the PCBF safety filter problem with zero constraint violation. The convergence result relies on a novel class mathcal{K} lower bound on the PCBF decrease and depends on the approximation error of the neural network. Lastly, we demonstrate our approach in simulation for an autonomous driving example and show that the proposed approximation leads to a significant decrease in computation time compared to the original approach.

Keywords: Modeling, Markov processes, Complex systems
Abstract: — In this paper, we propose a composition framework for stochastic safety of distributed Markov Decision Processes (MDPs) and Partially Observable Markov Decision Processes (POMDPs). We use MDP and POMDPs and their distributed version as an appropriate modelling paradigm for computational ecosystems, understood in the context of distributed systems. We extend our work on stochastic safety from MDPs to POMDPs, and then to networked MDP/POMDPs. We propose a unifying mathematical framework for stochastic safety for MDPs, their partially observable version and their composition.

Keywords: Automata, Optimal control, Large-scale systems
Abstract: In this paper, we consider a planning problem for a hierarchical finite state machine (HFSM) and develop an algorithm for efficiently computing optimal plans between any two states. The algorithm consists of an offline and an online step. In the offline step, one computes exit costs for each machine in the HFSM. It needs to be done only once for a given HFSM, and it is shown to have time complexity scaling linearly with the number of machines in the HFSM. In the online step, one computes an optimal plan from an initial state to a goal state, by first reducing the HFSM (using the exit costs), computing an optimal trajectory for the reduced HFSM, and then expand this trajectory to an optimal plan for the original HFSM. The time complexity is near-linearly with the depth of the HFSM. It is argued that HFSMs arise naturally for large-scale control systems, exemplified by an application where a robot moves between houses to complete tasks. We compare our algorithm with Dijkstra's algorithm on HFSMs consisting of up to 2 million states, where our algorithm outperforms the latter, being several orders of magnitude faster.

Keywords: Optimal control, Predictive control for linear systems, Distributed control
Abstract: The street lighting system, as one of the main infrastructures of modern urban cities, is widely distributed, technologically outdated, and poorly controlled. With several thousands of lamps, a real-time centralized control system is technologically and economically challenging. This paper presents a distributed model predictive control algorithm to adjust the illuminance intensity level, i.e. the dimming profile, at chosen spatial points of interest in a street lighting system. The algorithm is formulated as a trajectory-tracking quadratic problem with a central coordinator to deliver dynamically-calculated references at points of interest based on predicted micro location data of traffic volume, pedestrian density and diversity, and weather conditions. Mathematical model of the ray tracing spatial light propagation to ensure horizontal and vertical lighting quality for various urban traffic participants, as well as the reduction of lighting pollution, while improving the energy efficiency of the whole infrastructure. The controller is designed by respecting a premise of the low-cost implementation to large number of distributed lamps. The simulation results indicate that the proposed algorithm achieves a good performance of the whole system with limited burden of communications.

Keywords: Network analysis and control, Control over networks
Abstract: We introduce a multi-layer networked compartmental SIRS-V_o model that captures opinion dynamics, disease spread, risk perception, and self-interest vaccine-uptake behavior in an epidemic process. We characterize the target vaccination criterion of the proposed model and conditions that guarantee the criterion is obtainable by influencing opinions on disease prevalence. We leverage this result to design an eradication strategy that leverages opinions and vaccination. Through numerical simulations, we show that the proposed eradication strategy is able to stabilize the epidemic process around a healthy state equilibrium, and the outbreak rebounds after the control signal is relaxed.

Keywords: Optimization, Manufacturing processes, Computational methods
Abstract: This work aims at contributing to the advancement of Logistics 4.0, focusing on the management of the storage of goods. The goal is to solve the complex problem of efficiently and rapidly configuring Vertical Lift Modules (VLMs) with sliding trays in automated warehouses. This problem is still barely discussed in the related literature and most contributions mainly focus on the optimization of the VLM throughput instead of trays allocation and respective items configuration. To fill this gap, this work proposes a novel mathematical model that allows to properly represent and solve this complex problem, taking into account practical logistic constraints. The problem is defined as a mixed integer non-linear programming model, which is validated on realistic scenarios. Further, a scalability analysis is performed to evaluate its performance even in complex scenarios. The obtained results demonstrate the effectiveness of the model in defining space efficient configurations in short computation time.

Keywords: Optimal control, Distributed control, Agents and autonomous systems
Abstract: In this paper we propose a comparative analysis of control efficiency for several methods suitable for a vehicle platooning application. To this end, we compare the results obtained with both Distributed Model Predictive Control strategy and Coalitional Control using an optimal feedback gain matrix control methodology. The string stability of the platoon is ensured via an infinity-norm longitudinal error constraint imposed in the optimization problem. Our results suggest that both control strategies give similar control performances, at different computational efforts.

Keywords: Automotive, Optimal control, Optimization
Abstract: Catenary overhead lines have recently drawn attention as a means of extending the range of battery electric trucks. In order to characterize the potential stemming from such an infrastructure, our paper presents a framework to optimize the routes of battery electric trolley trucks jointly with their driving and charging strategies, whereby both static and dynamic charging is possible by either stopping at a station or by connecting to the catenary line, whenever present, while driving. Specifically, we first devise a drive cycle convex optimization model to obtain a Pareto front of optimal strategies in terms of energy usage and travel time. Thereafter, we compute such Pareto fronts for all road links present on a given road network, and leverage them to frame a time-optimal routing problem accounting for battery consumption and charging as a mixed-integer linear program that can be efficiently solved with global optimality guarantees. Our results for the BeNeLux highway network show that catenary lines can significantly reduce the travel time of battery electric trolley trucks, making them a promising alternative to internal combustion engine trucks.

Keywords: Concensus control and estimation, Distributed cooperative control over networks, Cooperative control
Abstract: This paper considers a leader-following consensus in the presence of unknown but bounded cyber-attacks. Specifically, we consider the following cyber-attack scenarios:(i) an attacker aims to destabilize the consensus dynamics by injecting exogenous signals to both the actuators of the followers and/or the communication network,(ii) an eavesdropper adversary aims to obtain information on the physical state of the agents. To this end, a novel resilient leader-following consensus algorithm based on a competitive interaction method is proposed. In addition, it is demonstrated that by appropriately choosing the information exchanged between the agents, the proposed control framework also enables the cooperative system to either distributively identify the compromised communication links in real-time or to protect the privacy of the physical state of the agents from the eavesdropper. A numerical example is provide to illustrate the proposed resilient control algorithms.

Keywords: Hybrid systems, Filtering, Identification for hybrid systems
Abstract: The massive digital transformation of industrial control systems, based on tight integration between information and telecommunication technologies, has enlarged the attack surfaces of the industrial systems, thus increasing the need to protect the operational and production processes. Information Technology tools are currently not able to guarantee confidentiality, integrity, and availability in the industrial domain, therefore it is of paramount importance to understand the interaction of the physical components with the information networks. This paper aims to provide a novel Intrusion Detection System for identifying cyber-attacks in cyber-physical systems: the key novel idea is to consider a virtual model for both the plant and the controller to detect attacks. In fact, most of the approaches presented in literature rely on a virtual model representing only the plant, while in this contribution we consider the plant and the controller as a system to be replicated in the digital twin and resulting in a hybrid automata. This allows the detection of attacks to the actuators, that otherwise cannot be revealed. Advantages of this approach are demonstrated by exploiting data from a water distribution system.

Keywords: Distributed estimation over sensor nets, Agents networks, Agents and autonomous systems
Abstract: The paper considers the problem of propagation and detection of false information in a distributed sensor network tasked with multi-object tracking. Leveraging the framework of multi-object tracking by means of the ac{phd} filter, the papers contributes twofold. First, we proposed a new byzantine attack called the overconfident data falsification attack that exploits the knowledge of the data fusion protocol to feed the network with false low uncertainty estimates. Second, we devise a defense strategy within the fusion protocol by introducing time-varying fusion weights that use an inter-agent trust measure based on the Beta reputation system to decide the level of information merging from each neighbouring agent.

Keywords: Fault diagnosis, Fault detection and identification, Computational methods
Abstract: Cause and effect analyses can reveal useful insights about the information flow within process alarm data, which can, in turn, be used to improve the process performance. Cause and effect analyses require selecting suitable time windows of the historical alarm data, as performing the analysis on the entire time horizon or unsuitable windows might negatively influence the analysis due to noise. This motivates the combination of alarm flood identification, clustering, and cause and effect analysis.

Hence, we propose to combine a causal inference analysis based on Direct Transfer Entropy (DTE) with alarm flood detection and clustering to perform the DTE analysis on reasonable time intervals. Our approach performs an alarm data clustering first to find intervals of clusters of similar alarm floods. Afterward, we calculate the DTEs on the found alarm flood cluster intervals.

Our results show that the combination of clustering and cause and effect analysis reveals results that were not feasible before. This facilitates analyzing the causal relations in the alarm data. The approach reduces the noisy influence of random alarms on the cause and effect analysis, and provides a clearer structure of the causal relations in the alarm data.

Keywords: Fault diagnosis, Uncertain systems, Fuzzy systems
Abstract: Ranking failure modes in complex system is important for analysis, diagnosis and prevention of risks in practical engineering. Failure mode and effects analysis (FMEA) theory provides the risk priority number (RPN) for ranking of failure modes. However, researches find that some shortcomings exist in the classical RPN methods of FMEA. For example, the uncertainty among FMEA experts is not modeled properly. To address this issue, we propose the belief divergence weighted risk priority number (BDWRPN) for failure modes ranking in FMEA in the framework of Dempster-Shafer evidence theory (DST). The belief divergence measure in DST is adopted to measure the uncertainty judgements from FMEA experts. The Deng entropy in DST is designed to measure the relative importance among FMEA experts. The Dempster's combination rule in DST is adopted for fusion of assessments on each risk factor coming from different FMEA experts. A case study of failure modes ranking for a sheet steel production process is designed to verify the effectiveness of the proposed method.

Keywords: Hybrid systems, Sliding mode control, Neural networks
Abstract: This paper investigates the sliding mode control (SMC) against actuator attack problem of uncertain singular semi-Markov jump systems (singular S-MJSs) with time-varying delay and exogenous disturbance under partially unknown transition rate matrix. The specific information of actuator attack and bounds of disturbance term are unknown in the controller design process. Moreover, any knowledge of unknown elements existing in partially unknown transition rate matrix is not required. Our attention is mainly focused on designing a novel adaptive H∞ neural sliding mode controller for such complex systems. Firstly, a distinctive “random switch”-triggered stochastic sliding surface is put forward, which is different from traditional integral-type sliding surface design. Secondly, under missing information of transition rate matrix, H∞ stochastic admissibility analysis is carried out and a corresponding sufficient condition is established, further, a corresponding algorithm including solvable linear matrix inequalities (LMIs) is proposed to determine the controller gain. Thirdly, an adaptive H∞ neural sliding mode controller, which can estimate unavailable parameter bounds in real time and introduces an ingenious neural network to approximate the actuator attack, is designed such that the singular S-MJSs are insensitive to all admissible uncertainties and can be stabilized under the actuator attack. The stochastic admissibility with desirable H∞ performance of the closed-loop system can be guaranteed. Finally, DC motor system is simulated to demonstrate the effectiveness of the proposed results.

Keywords: Transportation systems, Predictive control for nonlinear systems, Optimal control
Abstract: Trains are a corner stone of public transport and play an important role in daily life. A challenging task in train operation is to avoid skidding and sliding, i.e. spinning or blocking of the wheels, during fast changes of traction conditions, which can, for example, occur due to changing weather conditions, crossings, tunnels or forest entries. The latter depends on local track conditions and can be recorded in a map together with other location-dependent information like speed limits and inclination. In this paper, a model predictive control (MPC) approach is developed. Thanks to the knowledge of future changes of traction conditions, the approach is able to avoid short-term skidding and sliding even under fast changes of traction conditions. In a first step, an optimal reference trajectory is determined by a multiple-shooting approach. In a second step, the reference trajectory is tracked by an MPC setup. The developed method is simulated along a track with fast-changing traction conditions for different scenarios, like changing weather conditions and unexpected delays. In all cases, skidding and sliding is avoided.

Keywords: Traffic control, Transportation systems, H2/H-infinity methods
Abstract: In this paper, we analyze a mixed platoon consisting of multiple human-driven vehicles (HDVs) and a number of connected and automated vehicles (CAVs). The platoon is assumed to travel along a single-lane ring-road, with the CAVs uniformly distributed among the other vehicles and directly controlled by a centralized cooperative control strategy. Our primary objective is to develop a mixed H_2/H_{infty} control strategy capable of stabilizing traffic flow in the presence of undesired disturbances and noises. Furthermore, we demonstrate the efficacy of the proposed control method for various penetration rates of CAVs through numerical simulations.

Keywords: Traffic control, Distributed control, Predictive control for nonlinear systems
Abstract: Utilizing demand response (DR) in industries reduces the need for expensive utilities like storage or backup plants and renders the electricity market more flexible for industrial sites. This paper proposes an MPC-based approach for such sites to respond online to ancillary service requests and participate in DR by controlling optimally the machine speeds of a given number of production lines. We define centralized and distributed optimization programs to cope with a large number of machines within each line and with ancillary services that require a response within seconds (primary services) to several minutes (secondary services). We demonstrate the proof-of-concept by designing controllers for three beverage production lines consisting of filling machines, labelers, shrink packers, and several conveying belts. The efficiency of the algorithmic solution is illustrated by numerical simulations.

Keywords: Transportation systems, Autonomous systems
Abstract: Despite the extensive amount of research that exists regarding Connected and Autonomous Vehicles (CAVs), experimental results are few. Furthermore, most works assume an accurate vehicle model and either assume perfect localization or ensure that the localization accuracy is very high by conducting overly specific experiments or utilizing sensor setups that are difficult to be replicated in real scenarios. Motivated by these facts, we study an unsignalized intersection crossing problem with fully autonomous traffic and develop an online, safety-critical and model-free centralized controller for CAVs under imperfect localization. The centralized controller follows a First-Come-First-Served (FCFS) reservation policy and ensures no more than one vehicle is present inside the center of the intersection in order to avoid collisions. Realistic experiments are conducted with 1:10 scaled vehicles and the results are discussed.

Keywords: Transportation systems, Modeling, Network analysis and control
Abstract: This paper introduces a new model depicting electric vehicles (EVs) mobility and the evolution of their State-of-Charge (SoC) in urban traffic networks. The model couples the vehicles' mobility described by a set of dynamic equations over a graph capturing the Origin-Destination motion, with the energy consumption associated with the EVs mobility patterns. Additionally, power inputs from charging stations are included in the model. A model calibration method based on multi-source public data is also provided. Finally, several experiments are conducted through simulation to evaluate the appropriateness of the current charging station infrastructure under an increasing EVs penetration rate in the whole metropolitan area of Grenoble, France.

Keywords: Traffic control, Transportation systems
Abstract: Recent advancement of connected vehicle (CV) technology has promoted research on CV-based real-time traffic signal control. However, existing studies still suffer a low penetration rate and low positioning accuracy of the current CV data environment. To solve these limitations, we proposed a CV-based adaptive signal control method for urban intersections by considering the travel information of CVs at upstream intersections, which can predict vehicle arrivals over a long period and be applied to scenarios with low-penetration-rate and desired direction unknown CVs. Evaluation results show that the proposed method outperforms actuated control even with a 5% penetration rate under medium- and high-volume scenarios.

Keywords: Discrete event systems, Petri nets, Autonomous robots
Abstract: This paper proposes a high-level path planning strategy under Time Petri net (TPN) formalism for a multi - agent system, which is subject to Metric Interval Temporal Logic (MITL) specifications. The work aims to design a scalable model with respect to the number of agents, as the MITL formula requires multiple agents to ensure similar tasks. The obtained model is denoted Composed Time Petri net and it couples two TPN representations assigned to the motion of the agents, respectively to the MITL specification. The planning approach is based on model-checking methods and the results are evaluated on a case study applied in robotics industry.

Keywords: Autonomous systems, Output feedback, Optimization
Abstract: This paper considers the problem of optimizing robot navigation with respect to a time-varying objective encoded into a navigation density function. We are interested in designing state feedback control laws that lead to an almost everywhere stabilization of the closed-loop system to an equilibrium point while navigating a region optimally and safely (that is, the transient leading to the final equilibrium point is optimal and satisfies safety constraints). Though this problem has been studied in literature within many different communities, it still remains a challenging non-convex control problem. In our approach, under certain assumptions on the time-varying navigation density, we use Koopman and Perron-Frobenius Operator theoretic tools to transform the problem into a convex one in infinite dimensional decision variables. In particular, the cost function and the safety constraints in the transformed formulation become linear in these functional variables. Finally, we present some numerical examples to illustrate our approach, as well as discuss the current limitations and future extensions of our framework to accommodate a wider range of robotics applications.

Keywords: Coverage control, Agents and autonomous systems, Robotics
Abstract: This paper studies the trajectory of a pair of unicycles pursuing each other. The control law depends only on the distance between them. The resulting paths trace hypotrochoidal patterns. These patterns are suitable for coverage path planning and can be utilized in surveillance, patrolling, demining, and similar applications. We find the conditions under which unicycles generate the patterns. We also design the controller gain that ensures the patterns cover a region with a specified maximum and minimum radii. The results are validated in simulations.

Keywords: Robotics, Randomized algorithms, Autonomous systems
Abstract: In this paper, we study the asymptotic optimality property of a randomized incremental sampling based motion planner, namely RRT. The study proves that an RRT planner is not an asymptotically optimal motion planner. The result, while being consistent with similar results that exist in the literature, however, brings out an important characteristic of an RRT planner, that is, the degree distribution of the tree vertices follows a power law in an asymptotic sense. Simulation results are shown to support the theoretical claim. Based on these results, this work establishes a simple necessary and sufficient condition for sampling based motion planners to be asymptotically optimal.

Keywords: UAV's, Stochastic filtering, Stochastic systems
Abstract: Navigation with a Phased Array Radio System (PARS) is prone to performance degradation due to radio wave reflections of the directed communication beam on reflective structures. This work presents a navigation algorithm, based on the Error State Kalman Filter (ESKF) and an outlier rejection scheme, to handle such outliers better and improve navigation robustness. We show, with experimental data, that this algorithm results in an improved state estimation quality when radio beam reflections are present.

Keywords: Modeling, Linear systems, Stability of nonlinear systems
Abstract: In this paper, the problem of modeling and stabilization of a quadrotor transporting cable-suspended load are investigated. Based on the Lagrange d’Alembert formulation, the system modeling the quadrotor with cable suspended load is presented. In the (XGZ) plane, we use backstepping control method and Lyapunov theory to stabilize the study system in the neighborhood of a desired position and attitude. Next, we illustrate how to planning and tracking a reference trajectory of quadrotor transporting cable-suspended load system based on the property of differential flatness. In the (YGZ) plane, we use input-output linearization to stabilize the system. Simulations are performed to prove the performance of the proposed controllers.

Keywords: Lyapunov methods, Decentralized control, Large-scale systems
Abstract: We prove a new theorem on ell_{infty}-fixed-time uniform ISS decentralized stabilization of infinite networks composed of interconnected switched nonlinear systems with unknown switching signals. Each subsystem has the lower-triangular form with uncontrollable linearization, more specifically it has the form of the so-called ``generalized polynomial integrator''. To solve this problem, we use a new small gain theorem on ell_{infty}-fixed-time uniform input-to-state stability of infinite networks and redesign several previous backstepping algorithms of decentralized stabilization. In particular, our main result generalizes several recent related ones.

Keywords: Lyapunov methods, Stability of nonlinear systems
Abstract: This paper deals with the stability analysis of the interconnection between a linear system and a nonlinear operator. This operator is the combination between an asymmetric backlash and a dead-zone. It is a memory-based operator which is only piecewise differentiable. To handle such a nonlinearity, which is more complex than the standard ones, we aim to capture the closed-loop trajectories in some suitable regions of the state space. Uniform Ultimate Boundednes (UUB) property is addressed, using the intrinsic behavior of the operator and a Lyapunov-based approach. Sufficient conditions are provided in terms of Linear Matrix Inequalities (LMIs). A numerical illustration is presented to assess our contribution.

Keywords: Sliding mode control, Neural networks
Abstract: This paper proposes an enhanced version of the integral sliding mode (ISM) control, where a deep neural network (DNN) is first trained as a deep reinforcement learning (DRL) agent. Then, such a DNN is fine-tuned relying on a Lyapunov-based weight adaptation law, with the aim of compensating the lack of knowledge of the full dynamics in the case of robot manipulators. Specifically, a DRL agent is trained off-line on a reward depending on the sliding variable to estimate the unknown drift term of the robot dynamics. Such an estimate is then exploited to initialize and perform the fine tuning of the online adaptation mechanism based on the DNN. The proposal is theoretically analysed and assessed in simulation relying on the planar configuration of a Franka Emika Panda robot manipulator.

Keywords: Switched systems, LMI's/BMI's/SOS's
Abstract: With the objective of developing computational methods for stability analysis of switched systems, we consider the problem of finding the minimal lower bounds on average dwell-time that guarantee global asymptotic stability of the origin. Analytical results in the literature quantifying such lower bounds assume existence of multiple Lyapunov functions that satisfy some inequalities. For our purposes, we formulate an optimization problem that searches for the optimal value of the parameters in those inequalities and includes the computation of the associated Lyapunov functions. In its generality, the problem is nonconvex and difficult to solve numerically, so we fix some parameters which results in a linear program (LP). For linear vector fields described by Hurwitz matrices, we prove that such programs are feasible and the resulting solution provides a lower bound on the

Keywords: Computational methods, Computer aided control design, Stability of nonlinear systems
Abstract: We present computer-assisted proofs for Lyapunov stability theorems for autonomous discrete-time dynamical systems. Our approach is supported by the concrete formally-verified realization of the exact real arithmetic. The selected variants of the Lyapunov stability theorems are reproduced within the proof system Minlog. Finally, we demonstrate how the formal proofs can be reused to prove other Lyapunov-related theorems and the soundness of discrete-time backstepping control design.

Keywords: Distributed parameter systems, Lyapunov methods
Abstract: In this paper, we treat the problem of Lyapunov-based nonlinear boundary stabilization of a class of one dimensional reaction-diffusion systems with any predefined convergence (asymptotic or non-asymptotic). As an application, we focus on the non-asymptotic notions (finite-time and fixed-time) for which we give some particular explicit control designs followed by some numerical simulations. The key idea of our approach is to use a ``spatially weighted L^2-norm" as a Lyapunov functional to design a nonlinear controller and to ensure the stability with any desired convergence.

Keywords: Filtering
Abstract: The performance of a discrete time Kalman filter in the presence of a misspecified measurement equation is considered. Analytical and easily calculable numerical bounds for the increment in filtering error energy are provided in an adversarial setting by formulating a high-dimensional optimization problem which is solvable via its Lagrange dual, a scalar convex optimization problem. The performance bounds are obtained for finite and infinite horizons.

Keywords: Optimal control, Stability of linear systems
Abstract: In this paper, we focus on the French Macroeconomic model. We use real economic data, available as time series, starting from 1980s and openly provided by the INSEE. Variables such as Gross Domestic Production, Exportation, Importation, Household Consumption, Gross Fixed Capital Formation and Public expenditure are included in the analysis. Our objective is to maintain a constant economic growth rate according to the available resources. We implement an optimal control policy via LQR to achieve that. Since we aim to maintain a constant growth rate, the control system is modified for this purpose. We prove the efficiency with three experiments based on real data, and we test the method robustness with respect to: (1) variation of LQR parameters, (2) realistic constraints on inputs, and (3) perturbations on outputs. Results show that our designed

Keywords: Robotics, Stability of nonlinear systems, Switched systems
Abstract: We consider the problem of image based visual servoing (IBVS) under inelastic visibility constraints that arise from the field-of-view (FOV) of the camera. The target is affected by possible aperiodic impulse perturbations. Under the assumption that a minimum time needs to elapse after each impulse, the proposed controller guarantees that all image feature will remain strictly inside the FOV and will converge to the desire feature values with prescribed performance characteristics, related to accuracy and convergence time, between any two consecutive impulses. Simulation results clarify and verify the theoretical findings.

Keywords: Adaptive control, Lyapunov methods, Output feedback
Abstract: The industry utilises hydraulic actuators due to their high power density and robustness. Especially the control of mobile hydraulic systems is challenging, among others, because of unknown and time-variant system parameters and the limited use of sensors. Firstly, parameters such as viscous damping, inertia and external load might change during operation and need to be estimated. Secondly, mobile hydraulic machines are commonly equipped with pressure sensors only but lack position or velocity sensors because of high cost and poor robustness. From a control perspective, systems with uncertain parameters are typically addressed by adaptive control methods to estimate uncertainties and stabilise the system. As the primary objective is to track the velocity, an observer for the uncertain system is necessary, which is not common in adaptive control. This paper presents an adaptive and command filtered backstepping algorithm, including an observer for the uncertain system for a class of hydraulic systems with fast valve dynamics. The algorithm adapts physically meaningful parameters and observes the velocity of the uncertain system. In addition, the adaptation laws are reformulated by partial integration to remove the requirement to measure the velocity ultimately. Stability is proven based on Lyapunov theory, and performance is demonstrated on a nonlinear simulation model of an exemplary hydraulic system.

Keywords: H2/H-infinity methods, Feedback linearization, Robust control
Abstract: In this study, a multi-objective robust H2/H∞ controller for a half-car model of an active inerter-based suspension system in the presence of external disturbance has been investigated. Its main goal is to improve the inherent trade-offs between ride quality, handling performance, and suspension travel and to guarantee the allowable level for suspension stroke and energy consumption. Inerters have been widely used to suppress undesirable vibrations of various types of mechanical structures. The advantage of inerters is that the realized equivalent mass ratio (inertance over primary structure mass) is greater than its actual mass ratio, leading to higher performance for the same effective mass. First, the dynamics and state space of the active inerter-based suspension system for a half-car model have been achieved. In order to attain the defined objectives, and ensure that the closed-loop system achieves the prescribed disturbance attenuation level, the Lyapunov stability function, and linear matrix inequality (LMI) techniques have been utilized to satisfy the multi-objective robust H2/H∞ criterion. Furthermore, to limit the gain of the controller, some LMIs have been added. In the case of feasibility, sufficient LMI conditions by solving a convex optimization problem afford the stabilizing gain of the constrained robust state-feedback controller. Numerical simulations show that the active inerter-based suspension system performs much better than a passive suspension with inerter and active suspension without inerter.

Keywords: Concensus control and estimation, Distributed cooperative control over networks
Abstract: Formation control, incorporating the nonholonomic constraints upfront, is a challenging task. At the individual agent level, Brockett’s theorem states that nonholonomic systems cannot be stabilized to a point using smooth, and timeinvariant feedback control law. We present a solution to the formation problem in a geometric framework by incorporating the constraint distribution at an individual agent level that obeys each agent’s nonholonomic constraints. The objective of the consensus is then tackled by introducing a suitable Morse function. Critical points of the chosen Morse function form a consensus manifold. We take inspiration from the consensus law on Euclidean space and draw a comparison. The negative gradient of the Morse function forms the proportional control leading the trajectory to the set of critical points, which is the same as the consensus manifold. In addition, we propose a corollary that enables a priori specified orientation regulation in a set of wheeled mobile robots. These findings are verified using several numerical experiments with random initial conditions.

Keywords: Electrical power systems, Power plants, Power electronics
Abstract: Electrification is considered as the greatest engineering achievement of the 20th Century by the US National Academy of Engineering. This academy acknowledges that large power systems are the most complex machines ever built by mankind. They are the most emblematic examples of system of systems: thousands of large generating units interacting with millions of electrical loads through long distance connections.The electrical grids and their management become more and more complex. This state of affairs has different causes that will not disappear in the future. The first reason is the massive integration of renewable but generally intermittent generation and the associated consequences: power electronics, storage, demand side management. The second main reason is that it is more difficult than ever to build new overhead lines because of low public acceptance. The third reason is linked to the setup of electricity markets crossing the administrative borders; they are global optimizers pushing the system towards its limits. The last reason is that the ageing of grid assets needs increasing attention. A significant part of the European grids’ assets are more than 50 years old. This presentation focuses on power system operation and control. We start by presenting ideas on a new control architecture, the new role of human operators in the process as navigators rather than pilots, and some examples of ongoing projects. We discuss open research challenges. One is related to approximation and to reduction of large complex systems. Another is the control of large populations of devices with a partial autonomy. We conclude with some ideas on machine learning approaches, whose lack of certificates is a great barrier for their adoption for controlling critical systems.

Keywords: Electrical power systems, Power plants, Power electronics
Abstract: Controllers for dynamical systems that operate in safety-critical settings must account for stochastic disturbances. Such disturbances are often modeled as process noise in a dynamical system, and common assumptions are that the underlying distributions are known and/or Gaussian. In practice, how- ever, these assumptions may be unrealistic and can lead to poor approximations of the true noise distribution. We present a novel controller synthesis method that does not rely on any explicit repre- sentation of the noise distributions. In particular, we address the problem of computing a controller that provides probabilistic guarantees on safely reaching a target, while also avoiding unsafe regions of the state space. First, we abstract the continuous control system into a finite-state model that cap- tures noise by probabilistic transitions between discrete states. As a key contribution, we adapt tools from the scenario approach to compute probably approximately correct (PAC) bounds onthese transition probabilities, based on a finite number of samples of the noise. We capture these bounds in the transition probability intervals of a so-called interval Markov decision process (iMDP). This iMDP is, with a user-specified confidence probability, robust against uncertainty in the transition probabilities, and the tightness of the probability intervals can be controlled through the number of samples. We use state-of-the-art verification techniques to provide guarantees on the iMDP and compute a con- troller for which these guarantees carry over to the original control system. In addition, we develop a tailored computational scheme that reduces the complexity of the synthesis of these guarantees on the iMDP. Benchmarks on realistic control systems show the practical applicability of our method, even when the iMDP has hundreds of millions of transitions.

Keywords: Electrical power systems, Power plants, Power electronics
Abstract: Congestion problems are increasing in number in power transmission networks due to the increment of renewable power sources along it. To reduce their impact, Transmission System Operators (TSOs) use network reconfiguration or renewable power curtailment in complex subtransmission areas. The operators need enhanced methodological tools to better address the optimal power flow management problem by also using novel levers as for example the storage devices. This talk proposes mathematical models which integrate the possibility to partially curtail the renewable power in the form of a dynamical system representing the transmission network, also considering storage devices.

Keywords: Electrical power systems, Power plants, Power electronics
Abstract: In order to minimize curtailment of renewable generation and to achieve effective grid voltage regulation and line congestion control, transmission system operators need to tap into the flexibility of active power distribution grids. We model this multi-player decision problem as a hierarchical game that accounts for the competitive interests of the different operators. We consider iterative algorithms that are guaranteed to converge to the efficient equilibrium of this hierarchical game and we turn them into feedback control strategies. These strategies allow the transmission system operator to react in real-time to grid measurements and to coordinate the optimal response of the distribution networks via suitable incentives. We discuss the theoretical guarantees associated to this feedback equilibrium seeking scheme and how this framework can be used to design effective and fair incentives.

Keywords: Electrical power systems, Power plants, Power electronics
Abstract: Blackstart procedures have historically relied on conventional power plants. At the same time, power electronics are becoming ubiquitous as they replace conventional synchronous machines as the main building block for power systems, and they are expected to contribute towards grid stabilization. In this sense, it is envisioned that inverter-based resources will also be responsible for grid recovery in the near future, despite their limited capabilities in terms of rated power and energy. This is nonetheless also an opportunity to fully utilize the flexibility and controllability of inverters in order to improve and speed up existing strategies. In this talk we propose a coordinated approach where so called grid-forming inverters are able to reenergize large grids without the presence of synchronous machines.

Keywords: Optimization algorithms, Signal processing, Reduced order modeling
Abstract: Hyperspectral imaging is one of the advanced remote sensing techniques whose goal is to obtain the spectrum for each pixel in the image of a scene, with the purpose of finding objects, identifying materials or detecting processes. However, the high dimensional nature of hyperspectral images makes their analysis complex. Various methods have been developed to reduce the dimension of hyperspectral images. Most commonly used dimension reduction techniques are Principal Component Analysis (PCA) and Independent Component Analysis (ICA). PCA is a method to reduce the dimensionality by removing the correlation among the bands, while ICA finds additively independent components. FastICA is one of the most used ICA algorithms. It is based on maximizing a loss derived from the fourth order statistical moment (kurtosis) or negentropy, which are both non-convex functions. Moreover, FastICA can find irrelevant stationary points (no maxima) and is not scalable as it uses at each iteration the whole set of pixels. In this paper, we present a stochastic second-order Taylor-based algorithm adapted to such ICA non-convex loss functions. Our algorithm guarantees ascent, hence it usually identifies (local) maxima. Moreover, the algorithm since it is stochastic, is scalable. Detailed numerical simulations show the superior performance of our method compared to FastICA.

Keywords: Machine learning, Statistical learning, Optimization algorithms
Abstract: Kernel methods provide a powerful framework for non parametric learning. They are based on kernel functions and allow learning in a rich functional space while applying linear statistical learning tools, such as Ridge Regression or Support Vector Machines. However, standard kernel methods suffer from a quadratic time and memory complexity in the number of data points and thus have limited applications in large-scale learning. In this paper, we propose Snacks, a new large-scale solver for Kernel Support Vector Machines. Specifically, Snacks relies on a Nyström approximation of the kernel matrix and an accelerated variant of the stochastic subgradient method. We demonstrate formally through a detailed empirical evaluation, that it competes with other SVM solvers on a variety of benchmark datasets.

Keywords: Optimization, Computational methods, Modeling
Abstract: It is shown that the behavior of an m-port circuit of maximal monotone elements can be expressed as a zero of the sum of a maximal monotone operator containing the circuit elements, and a structured skew-symmetric linear operator representing the interconnection structure, together with a linear output transformation. The Condat–Vũ algorithm solves inclusion problems of this form, and may be used to solve for the periodic steady-state behavior, given a periodic excitation at each port, using an iteration in the space of periodic trajectories.

Keywords: Optimization, Control over networks, Agents networks
Abstract: Mixed-Integer Programming (MIP) is an established method used to develop Multi-Agent System (MAS) motion planning algorithms under particular requirements, such as Line of Sight (LOS) connectivity. However, maintaining the tractability of the ensuing models is a known issue. In this paper, we propose a novel sufficient condition for LOS between agents of a MAS and discuss its implementation in a (MIP) model as constraints. The functionality of the proposal is demonstrated through simulations in a connectivity-constrained motion planning and decision-making problem. A comparison between the novel method and an earlier approach shows that the new proposal requires fewer optimization variables and constraints to be implemented. Finally, we employ Monte Carlo simulations to demonstrate that this reduction in the complexity of the models consistently decreases the time required to solve them.

Keywords: Optimal control, Distributed control, Optimization algorithms
Abstract: We present a novel optimal control scheme for coordinating pressure and self-cleaning operations in water distribution networks. In particular, we introduce a time-linking constraint on pressure variation to limit hydraulic dynamics. The resulting problem is nonseparable and therefore becomes difficult to solve for large-scale operational networks. To improve computational performance, we implement the alternating direction method of multipliers (ADMM) algorithm, which decomposes the nonlinear control problem into a series of smaller (and distributed) subproblems. Preliminary results on a large-scale operational network show significant improvements in computational performance.

Keywords: Optimal control, Predictive control for nonlinear systems, Robotics
Abstract: A path following feedback controller for mobile robots based on model predictive control (MPC) is introduced. The path following problem is formulated in the Frenet-Serret frame with the predictive controller aiming to drive the mobile robot approach and follow a parametrized geometric path while maximizing the robot speed, i.e., the covered robot distance. Since hyperparameters of the MPC formulation greatly affect the tradeoff between path-tracking error and maximum possible speed, we propose a Bayesian optimization-based algorithm to automatically select suitable parameters that maximize the speed while keeping the tracking error low. The algorithms are implemented on an omnidirectional mobile robot and validated using different predefined geometric paths. Finally, we compare the performance of the robot with manual tuning and automatic tuning, thereby illustrating the effectiveness of the latter.

Keywords: Optimization, Optimal control, Optimization algorithms
Abstract: This paper deals with the optimization of a sewer network using a Sliding Window-based Particle Swarm Optimization (PSO) algorithm. The sewer network corresponds to a city with a population of 250,000 inhabitants, its model being implemented in BSM Sewer. The optimization of the sewer network is done with respect to the amount of the discharged water minimizing this way the environmental impact of the wastewater collecting and transport systems. In the simulations, an influent containing three components was considered: domestic, pluviometric and industrial influent. Daily weather forecast for influent was considered. The control strategy proposed in this paper is based on the calculus of the optimal control actions for each individual tank with respect to the liquid level in that tank. The optimal controls were determined over a 24-hour sliding time window. The optimization algorithm was run over a time horizon of 28 days, during which two events were considered, one of rain and one of storm. As the daily forecast of the influent is not 100% accurate, a disturbed influent was considered to observe if the performance of the algorithm is maintained in this scenario. The simulation results were compared with those obtained in the operating mode “no control” (all control actions set at 100%).

Keywords: Markov processes, Optimization
Abstract: We consider a constrained Markov decision process (CMDP) with total reward criterion under random reward and cost parameters, and known transition probabilities. The CMDP problem is formulated as a joint chance-constrained Markov decision process (JCCMDP) which captures the situation where the decision maker is interested in the payoff function that is obtained with certain confidence and the random constraints are jointly satisfied with a given probability level. When the reward and cost vectors follow elliptically symmetric distributions and dependence among constraints is driven by a Gumbel-Hougaard copula, we show that the upper and lower bounds to the optimal value of the JCCMDP problem is given by the optimal values of two second-order cone programming problems. As an application, we consider a budget optimization of the advertising platforms and perform numerical experiments on randomly generated instances.

Keywords: Delay systems, Linear systems
Abstract: The purpose of this note is to present the effects induced on the dynamics of a linear system using a derivative operator which is implemented by means of a delay-difference approximation. In this sense, a linear time-invariant (LTI) single-input/single-output (SISO) system is considered, subject to a PD-type controller for which the derivative action was implemented by considering the aforementioned mechanism. The use of such a methodology leads to a characteristic function with coefficients that explicitly depend on the delay parameter. This note presents sufficient conditions under which the approximation leads to unstable (real) roots, showing explicitly the sensitivity of the approximation with respect to “small” delays. Several illustrative examples complete the presentation.

Keywords: Signal processing, Iterative learning control, Adaptive control
Abstract: The paper describes a period/frequency adaptation mechanism applied to an active noise control scheme designed to attenuate disturbances with high autocorrelation characteristics. The proposed modification tracks slowly varying changes in the noise autocorrelation function peak. Moreover, it can switch between different algorithm settings when an abrupt change in noise characteristics is detected. This modification increases the method's robustness and the area of potential implementations. The algorithm's behavior and performance are verified with computer simulations using real-world signals and acoustic path models identified experimentally. The results confirm that the previously proposed ANC algorithm can be extended with the period tracking mechanism using little additional computational resources and without apparent degradation in attenuation or stability.

Keywords: Nonlinear system theory, Linear parameter-varying systems
Abstract: There is a growing interest in data-driven control of nonlinear systems over the last years. In contrast to related works, this paper takes a step back and aims to solve the output matching problem, a problem closely related to the reference tracking control problem, for a broader class of nonlinear systems called output-generalized bilinear (OGB), thereby offering a new direction to explore for data-driven control of nonlinear systems. It is shown that discrete-time linear parameter-varying systems are included in this model class, with affine systems easily shown to also be included. The proposed model class and method are illustrated using the simulation of a real-life system.

Keywords: Automotive, Robust control, Transportation systems
Abstract: This paper proposes a Linear Parameter Varying (LPV) based steering control design method, which contains data aided control elements, e.g., learning-based agents. The framework is based on a supervisory control structure, which contains a supervisor, a LPV controller and the data aided control element. The goal of this paper is to provide a safe steering control, with which the human steering intervention can be effectively imitated. Moreover, in the proposed framework the data aided control can be adapted to the actual requirements on driving style, without re-designing the LPV control. Thus, a general control structure with performance guarantees on path following constraints is provided, in which the data aided steering control element can be varied. The effectiveness of the proposed method through driver-in-the-loop scenarios is illustrated, in which different settings on the control system are analyzed.

Keywords: Autonomous systems, Predictive control for nonlinear systems, Computational methods
Abstract: A huge research effort is being spent worldwide by automotive companies and academic institutions for developing vehicles with high levels of autonomy, ranging from advanced driving assisted systems to fully automated vehicles. Nonlinear Model Predictive Control (NMPC) has the potential to become a key technology in this context, thanks to its capability to deal with linear and nonlinear systems, manage physical constraints and satisfy multi-objective performance criteria. However, NMPC is based on the online solution of a nonconvex optimization problem and this operation may require a high computational cost, compromising its real-time implementation. In this paper, we develop a “fast” data-aided NMPC approach, aimed at trajectory planning and control for autonomous vehicles. In particular, a Set Membership approximation method is used to derive from data tight bounds on the optimal NMPC control law. These bounds are used to restrict the search domain of the underlying NMPC optimization process, allowing a significant reduction of the computation time. The proposed NMPC trajectory planning and control approach is tested in simulation and compared with other state-of-the-art methods, considering a parallel parking maneuver.

Keywords: Adaptive control, Mechatronics, Modeling
Abstract: In this paper, on the basis of a recent idea using abstract rotations in system modeling, it is investigated whether this novel approach can replace the regression-based technique in data-driven system modeling and control. Via simulations made for a strongly nonlinear paradigm it was found that the fixed point iteration-based adaptive control that uses similar abstract rotations for system modeling and adaptive control is a promising candidate for the realization of this approach. The main novelty consists in the fact that this iterative adaptive method based on simple affine system model have never been brought into context with data driven techniques.

Keywords: Fault tolerant systems, Automotive, Robust adaptive control
Abstract: The paper presents a reconfigurable fault-tolerant control strategy for a semi-active suspension using magnetorheological (MR) damper. The aim of the control reconfiguration is to handle the adverse behaviour of the MR damper due to oil leakage induced by the wear of the suspension component. The proposed method relies on the data driven model of the MR damper, using an estimation procedure to quantify the healthiness of the damper and to estimate the performance degradation due to the oil leakage. The reconfiguration control strategy is founded on the Linear Parameter Varying (LPV) framework, where a scheduling variable is defined to represent the healthiness level of the MR damper. By the scaling of the control action through the scheduling variable, the performance degradation of the MR damper can be compensated to match the behaviour of the healthy dampers. The proposed method is demonstrated through simulations, comparing the performance of the fault-tolerant LPV control to conventional semi-active control methods.

Keywords: Stochastic control, Agents and autonomous systems, Safety critical systems
Abstract: This paper proposes a safe speed control strategy for autonomous vehicle approaching unsignalized crosswalks with pedestrians, which relies on a receding horizon stochastic model predictive control (SMPC). It is assumed here that cruising at a certain speed is a main vehicle preference, along with low acceleration values for improved comfort. This is used as a basis for related SMPC optimization problem formulation. Stochastic MPC variant is considered to account for uncertainty related to a pedestrian crossing decision. Related prediction horizon is divided into two phases, i.e., prior pedestrian reaching the crosswalk edge and after that. The cost function over the first phase is calculated online for different vehicle control parameters, while the cost for the second phase is derived from two offline derived maps corresponding to two scenarios, i.e., pedestrian opting for crossing prior to vehicle and pedestrian opting for yielding. The probabilities of related decisions for certain vehicle control parameters are obtained from a simple binary logistic regression model which is also used as a pedestrian decision model within simulations. SMPC is verified against a basic control strategy by means of large-scale simulations over a wide range of randomly generated initial conditions.

Keywords: Constrained control, Stability of linear systems, Optimization algorithms
Abstract: We consider the problem of designing controllers for linear time-invariant systems based on measured data (without explicitly identifying the matrices of the system). We discuss two methods for stabilization: forward and backward reachable set computations. In the former one, we collect trajectories of the system for different given initial states and compute control inputs enforcing some stability conditions. Then, using the offline computations, we design for any initial condition the online control inputs that stabilize the system. In the second approach, we synthesize offline a contracting set and use it to define online the system's control inputs. We have illustrated the efficiency of our results through an example.

Keywords: Linear systems, Stability of linear systems, Electrical power systems
Abstract: In linear time invariant (LTI) system design, the implementation of stabilizing state feedback based on eigenvalue assignment has been established as a standard. In this frame, a new solution is proposed with a twofold purpose: i) to decisively relax the high calculation effort needed in traditional pole placement techniques, especially for high-order multi-input systems, and ii) to simultaneously assign all the closed-loop system eigenvalues to have a desired common real part on the left-half complex plane. This is particularly useful in many real-world applications where dominant damping is a prerequisite condition for advanced designs of complex high- order systems. The proposed approach is based on the idea of utilizing Lyapunov techniques not only as examining methodologies of the system stability, but also as designing tools. In this framework, by keeping the predefined damping constant as the only parameter, the method is very simple since it needs the construction of a typical Lyapunov equation, for which it is a priori known that a solution exists. The proposed approach is validated on two practical examples from the area of power systems, firstly in the case of a multi-level converter and secondly in a dc microgrid application with two dc/dc buck converters feeding local constant current loads and linked through a distribution line.

Keywords: Safety critical systems, Model/Controller reduction, Electrical power systems
Abstract: Simulation functions are Lyapunov-like functions defined over the Cartesian product of state spaces of two (un)perturbed systems, a.k.a., concrete and abstract systems, to relate output trajectories of abstract systems to those of concrete ones while the mismatch between two systems remains within some guaranteed error bounds. In this work, we approximate concrete systems with abstractions with lower dimensions (reduced-order models) and develop robust simulation functions further to consider the perturbation in the abstract system by designing an interface function for the disturbance. The proposed approach allows concrete systems to have large disturbances, which is the case in many real-life applications, while noticeably reducing the closeness error between the two systems. Accordingly, this enables controller design using a reduced-order form of the concrete system and reducing the computational load required for formal synthesis. We demonstrate the efficacy of our approaches by synthesising a formal controller for a 9-state area of the known New England 39-Bus Test System, using only a 3-state abstract system.

Keywords: Agents and autonomous systems, Cooperative autonomous systems, Cooperative control
Abstract: In this paper, we investigate event-based multi-agent cyber-physical systems against an intelligent attacker. While event-based control has been used as a more secure approach against cyber-attacks in the literature, we will first discuss how a well-informed attacker with knowledge of the system dynamics can get around the benefits of event-based control. We will then provide a covert event-based attack against multi-agent cyber-physical system. A cyber-attack detection mechanism is then provided to detect these cyber-attacks by using a self-triggered control approach in the system.

Keywords: Electrical power systems, Distributed control, Cooperative control
Abstract: This paper focuses on the problem of resilient phase angle stabilization and frequency synchronization in converter-based microgrids, utilizing phasor measurement units (PMUs), in the presence of false data injection (FDI) cyber-attacks. The uniformly bounded cyber-attack signals are inserted to desynchronize converters and violate frequency constraints by manipulating control input channels. To tackle this issue, a resilient and robust cooperative angular control scheme is proposed by modifying the conventional angular control method and incorporating some auxiliary states interconnecting with physical states. By presenting the converter dynamics along with the proposed controller as a port-Hamiltonian (pH) system, the design considerations of the interconnection matrices are outlined. Theoretical analysis using input-output passivity and H_infty norm performance index are carried out to guarantee asymptotic stability and resilient frequency synchronization against FDI attacks. The performance and effectiveness of the proposed control scheme are evaluated through numerical simulations.

Keywords: Game theoretical methods, Optimization algorithms, Iterative learning control
Abstract: We consider a security game in a setting consisting of an attacker and multiple defenders. Both defenders and attacker have limited budgets such that they can select subsets of the nodes to defend and attack, respectively. Each node has a certain gain and loss to the attacker and the defender, respectively. Each node has also a probability of being successfully compromised, which is a function of the investments in that node by the defenders. For such games, we investigate the impacts of behavioral probability weighting on the strategies of the players; such probability weighting, where humans overweight low probabilities and underweight high probabilities, has been identified by behavioral economists to be a common feature of human decision-making. We explore the multi-round game under regret-matching and prove the convergence to correlated equilibrium in our game. We then show the effect of the behavioral bias on the convergence of the regret-matching learning. Finally, we show via numerical experiments that the regret-matching algorithm is effective for both behavioral and rational defenders (i.e., it helps both classes of defenders to converge to better investments over time).

Keywords: Computer aided control design, Process control, Control over networks
Abstract: Cyber-physical systems (CPSs) for real-time advanced process control (RT-APC) are a class of control systems using network communication to control industrial processes. In this paper, we use simple examples to describe the software principles and concepts used in the implementation of such systems. The key software principles are 1) shared data in the form of a database, files, or shared memory, 2) timers and threads for concurrent periodic execution of tasks, and 3) network communication between the control system and the process, and communication between the control system and the internet, e.g., the cloud to enable remote monitoring and commands. We show how to implement such systems for Linux operating systems applying the C programming language and we also comment on the implementation using the Python programming language. Finally, we present a complete simulation experiment using a real-time simulator.

Keywords: Optimal control, Constrained control, Automotive
Abstract: In this paper we propose a real-time maneuver generation algorithm for Autonomous Vehicles (AVs). Given a planar road geometry with static and moving obstacles along it, we are interested in finding collision-free maneuvers that satisfied the AV's dynamics and subject to physical and comfort limits. Based on longitudinal and transverse coordinates, we propose a novel collision avoidance constraint and formulate a suitable maneuver regulation optimal control problem. Maneuver regulation has intrinsic robustness with respect to standard trajectory tracking that stems from the requirement of following a desired path with a desired velocity profile assigned on it. The optimization problem is solved by using a nonlinear optimal control technique that generates (local) optimal trajectories. We demonstrate the efficacy of the proposed algorithm by providing numerical computations on two different scenarios. Finally, experimental results are presented to demonstrate the efficiency, both in terms of computational effort and dynamic features captured, of the proposed algorithm.

Keywords: Automotive, Autonomous robots, Machine learning
Abstract: With the recent advances in automated valet parking solutions on the mobility market for controlled environments, the attention on generalizing this solution in arbitrary conditions got into the focus as well. This relies on robust localization that mainly uses visual information, which is still challenging under harsh illumination and weather conditions. In this work, we present the results of the robustness analysis for image-based localization techniques in the context of automated valet parking based on Simultaneous Localization and Mapping. We evaluated the most promising methods from the state of the art, with a focus on the keypoint-feature descriptor robustness in challenging outdoor conditions. The evaluation benchmark, dataset, and framework are available on the author's webpage.

Keywords: Automotive, Autonomous systems, Optimal control
Abstract: This study proposes a Stanley controller for rear wheel steering (RWS) function development to improve lane keeping ability and serve ADAS/AD function development. Almost all RWS control applications that are implemented on commercialized vehicles are employed with reference following and feedforward control systems. Driver and vehicle inputs are used to generate reference targets and inputs to improve low-speed maneuverability and highway lateral comfort. In this study, we have proposed a Stanley controller to command rear wheel steering function to provide lane departure avoidance and enhance lane keeping performance. Lane departure warning systems and lane keeping assistance systems are activated in co-action mode in the driver assistance systems based on human-machine cooperation. In the co-action mode, driver and assistance devices act together on vehicle control. The simulation results reveal that Stanley controller can provide a robust and efficient solution with great error tracking and low control effort for the co-action control mode of the chassis control systems.

Keywords: Automotive, Modeling, Optimal control
Abstract: The energy management of parallel Hybrid Electric Vehicles (HEVs) is the problematic of determining at each instant the optimal torque split between the Internal Combustion Engine (ICE) and the Electric Motor (EM). Two opposite dynamics are related to this problem: the long dynamic of the battery State Of Charge (SOC) and the short dynamic of the ICE torque and speed variations due to power demand of the driver. Due to estimation and control errors in the air loop, these ICE variations of torque and/or speed lead to an imbalance in the air/fuel mix stoichiometry, causing an increase in pollutants generation. This paper aims to solving this issue by computing online optimal torque split taking into account the engine transients and their impacts on pollutants generation. The proposed Model Predictive Control (MPC) is based on Single Layer Perceptron pollutant model trained on experimental data. The control strategy used, when considering a certain prediction horizon, allows to reduce respectively by 0.9% the fuel consumption, by 23% the CO generation, by 11% the HC generation and by 31% the PM generation at the cost of an 8% increase in NOx generation.

Keywords: Autonomous systems, Intelligent systems, Automotive
Abstract: In this paper, we propose an end-to-end autonomous driving architecture for safe maneuvering in heterogeneous traffic using a reinforcement learning (RL) algorithm. Using the proposed architecture we develop an RL agent that can make driving decisions directly from the sensor data. We formulate the autonomous driving problem as a Markov Decision Process and propose different architectures using Deep Q-Networks for two types of sensor data - top view images of the autonomous vehicle (AV) and its surrounding vehicles and information on relative position and velocities of the surrounding vehicles w.r.t the AV. We consider a highway scenario and analyze the performance of the RL agent using the proposed architectures using the highway-env simulator. We compare the driving performance of the AV for both sensor types and discuss their efficacy under varying traffic densities.

Keywords: Predictive control for nonlinear systems, Automotive, Autonomous systems
Abstract: A truck platoon consists of several trucks moving together at high speeds with relatively small inter-vehicular spacing. In platoon formations, a key trade-off exists between energy efficiency that depends on the inter-vehicular spacing and safety that is related to the stability of the platoon. In an attempt to capture this trade-off, non-linear model predictive control has been proposed, considering detailed dynamics of on-road truck operation. The non-linear optimization problem that manifests has been solved numerically using the CasADi optimal control framework. The controller capabilities to establish stable platooning have been evaluated for typical accelerating and decelerating maneuvers and for a realistic heavy-duty highway drive cycle. The computational efficiency of the approach and its efficacy for real-time use have also been studied.

Keywords: Aerospace, UAV's, Power plants
Abstract: Dynamic soaring for UAVs is a flight technique that enables continuous, powerless periodic flight patterns in the presence of a wind gradient. However, sufficiently large wind gradients are uncommon over land, while at offshore locations the largest wind gradients are located close to the ocean surface, thereby limiting the scope of practical application. An intrinsic feature of wind turbines is that they inherently produce very sharp wind gradients in the near wake. Therefore, in this paper, we propose and investigate periodic stationary dynamic soaring trajectories in the near wake of wind turbines. We additionally consider the potential of dynamic soaring for revitalizing the wind turbine wake. To this end, we apply periodic optimal control based on a simplified model for the glider dynamics and the wind profile in the wake. The cost function maximizes the revitalization of the wake.

We compute optimal orbits for a range of different wing spans and different mass-scaling assumptions. The largest glider configuration, with a wingspan of 10 m and a mass of 222.6 kg, achieves a wake revitalization of about 0.94 % of the total turbine thrust.

Keywords: UAV's, Control over networks, Distributed cooperative control over networks
Abstract: Overhead transmission line monitoring and inspection to date is largely done by human examination. This is inefficient, costly and time consuming. This paper considers enabling a swarm of multicopters to continuously monitor the power network with the consideration of 1) the flight time and charging requirements of the swarm which is used to inform 2) the dynamic distribution of a swarm to autonomously survey the power network. To tackle these challenges we formulate a network time management and energy optimisation problem to minimise the overall network monitoring time and introduce a line graph advection protocol to autonomously distribute the multicopters across the power network. The approach is simulated on the Australia's east coast transmission line network to demonstrate the results.

Keywords: Aerospace, UAV's, Switched systems
Abstract: This paper presents the development of automated glide component of the Guidance, Navigation, and Control (GNC). The main focus is the initiation of the glide during loss-of-thrust (LOT). The best gliding airspeed is maintained during glide. Step-by-step procedure is used to reconfigure the controller by switching in the event of LOT, and detailed instructions are given. This method mimics the pilot during an emergency. This is crucial since the workload of the pilot increases significantly in an emergency. As a result, automation helps pilots, and they can use the suggested automated gliding method to give themselves more time to make decisions. Lateral navigation to the runway with line-of-sight (LOS) algorithm is being developed concurrently, and it uses the Dubins path. The controller consists State-Dependent Riccati Equation (SDRE), and incremental nonlinear dynamic inversion (INDI). With this reconfiguration, the aircraft glides to the runway safely. Results show that the proposed method is effective.

Keywords: Agents and autonomous systems, Autonomous robots, UAV's
Abstract: The target capture problem, i.e., the problem of reaching a target zone, by a mobile robotic agent that cannot sense its own global position requires reactive motion control algorithms based on onboard sensor data. Although the existing solutions to the target capture problem provide robust convergence guarantees, they do not address the mobile agent’s path and motion optimality. We address the agent path and motion optimality in target capture control and its extension to station keeping, i.e., steering the agent to a location that is pre-defined with respect to a set of beacons, in global positioning system (GPS)-denied environments. We formulate optimal control problems aiming to minimize the agent-target distance for target capture, and the difference of desired and actual agent-station distances for station keeping. We design and analyze a linear quadratic optimal control scheme involving a Luenberger observer based state estimator, for each of the target capture and station keeping problems. The proposed schemes outperform the previous approaches in numerical simulations in terms of agent path length and smoothness.

Keywords: UAV's, Aerospace, Autonomous systems
Abstract: In this work, a control strategy for landing a fixed-wing drone with classical configuration and with minimum airspeed on a touchdown point is presented. In this crucial and critic landing phase of this kind of aircrafts the challenge is to absorb the drone's airspeed without loss its controllability. Our strategy proposes a scientific solution for a safe landing by controlling the angle of attack of the drone assuring its stability during all this stage. In our analysis, a flight scheme composed by a cruise flight and a landing scheme is considered. The control strategy obtained from the Lyapunov theory proposes a critic descending angle to obtain a maximum airspeed reduction. In addition, an observer is proposed for estimating external aerodynamics parameters and compensate them in closed-loop system. Numerical validation corroborates the well performance of the proposed control algorithms.

Keywords: UAV's, Autonomous systems, Robotics
Abstract: Biological fliers such as birds are known for their bounding flight maneuvers during which they fold their wings under their bodies to soar intermittently, or manipulate their inertial body dynamics to achieve challenging trajectories. This combination of thrust vectoring and body control allows biological fliers to optimize for a wide number of objectives - ranging from aerodynamic drag minimization to maneuverability. However, combined posture control and thrust vectoring still remains largely unexplored in the aerial robotics community. In this paper, we use a dynamical model of an aerial robot with articulated thrusters to generate minimum time trajectories under spatially varying constraints. To this end, we formulate an optimal control problem that is solved numerically using trapezoidal collocation. Our results indicate that combining posture control and thrust vectoring can enable flying through narrow and spatially varying geometries as well as decreasing maneuver time by careful manipulation of shape inputs.

Keywords: Delay systems, Robust control, Algebraic/geometric methods
Abstract: The fundamental notion of relative degree is studied for a class of linear time-delay systems of retarded type, where the standard assumption of commensurate delays is dropped. Even though the analyzed systems do not have a non-trivial feedthrough from input to output, it is shown that the relative degree may be sensitive to infinitesimal delay perturbations. This observation is at the basis of a novel notion of relative degree, called strong relative degree, which is characterized algebraically and computationally. It plays a key role in guaranteeing well-posedness of systems controlled with extended PD controllers, for which a design procedure and numerical experiments are presented.

Keywords: Delay systems, Stability of linear systems
Abstract: This article focuses on the characterization of a particular spectral property called Multiplicity-induced-dominancy applying for linear dynamical systems described by delay-differential equations. More precisely, we characterize the property in the scalar neutral case with respect to the system parameters. Particular attention will be paid to over-order multiplicities corresponding to real double and triple characteristic roots.

Keywords: Output feedback, Delay systems, LMI's/BMI's/SOS's
Abstract: This note is concerned with presenting new dissipativity-based conditions for the stabilizability of discrete-time systems with time-varying delays by linear static output feedback (SOF). We demonstrate that the feasibility of nonlinear matrix inequalities for the design of feedback stabilizing gains derived from the literature is equivalent to the feasibility of a linear matrix inequality establishing dissipativity of the system plus one matrix inequality constraint. An iterative strategy allowing the computation of stabilizing SOF gains is then developed based on a new relaxed sufficient condition. Compared with other popular approaches in the literature, such as the celebrated cone complementarity linearization (CCL) method, the strategy avoids providing initial ``guesses'' for the matrix variables, which is especially complicated when dealing with a large number of matrices. Due to being a particular case of SOF with an identity output matrix, static state feedback (SSF) gains can also trivially be computed by exploiting the developed conditions.

Keywords: Delay systems, Robust control
Abstract: The focus of this research is to explore the robust stability of interval time delay systems that utilize delayed controllers. Specifically, the study assumes the use of a Proportional-Integral-Retarded (PIR) controller in conjunction with a first-order plus dead-time (FOPDT) model of the time delay system. The parameters of the system, including the gain, time constant, and time delay, are subject to interval uncertainties, which are a type of structured uncertainties for the plant model. To investigate the robust stability of the system, the study derives necessary and sufficient conditions, accounting for the presence of uncertainties. To facilitate the assessment of robust stability, the study also introduces a frequency range and robust stability checking function. Ultimately, the study aims to demonstrate the accuracy of its findings through the presentation of two numerical examples.

Keywords: Distributed estimation over sensor nets, Linear time-varying systems, Delay systems
Abstract: Distributed sensor networks often include a multitude of sensors, each measuring parts of a process state space or observing the operations of a system. Communication of measurements between the sensor nodes and estimator(s) cannot realistically be considered delay-free due to communication errors and transmission latency in the channels. We propose a novel stability-based method that mitigates the influence of sensor network delays in distributed state estimation for linear time-varying systems. Our proposed algorithm efficiently selects a subset of sensors from the entire sensor nodes in the network based on the desired stability margins of the distributed Kalman filter estimates, after which, the state estimates are computed only using the measurements of the selected sensors. We provide comparisons between the estimation performance of our proposed algorithm and a greedy algorithm that exhaustively selects an optimal subset of nodes. We then apply our method to a simulative scenario for estimating the states of a linear time-varying system using a sensor network including 2000 sensor nodes. Simulation results demonstrate the performance efficiency of our algorithm and show that it closely follows the achieved performance by the optimal greedy search algorithm.

Keywords: Delay systems, Stability of linear systems, Linear systems
Abstract: In this work, which is a natural continuation of [1] and [2], we show, that the coexistence of the maximal number of real spectral values of generic single-delay retarded second-order differential equations guarantees the realness of the rightmost spectral value. This is interpreted from a control theory point of view as, using a delayed PD controller, one can stabilize a second-order delay differential equation by assigning the maximal number of negative roots of the corresponding characteristic function (which is a quasipolynomial). We give a necessary and sufficient condition for the rightmost root to be negative and thus guarantee the exponential decay rate of the system solutions. We illustrate the proposed design methodology in the delayed PD control of the harmonic oscillator.

Keywords: Automotive, Constrained control, Autonomous systems
Abstract: The contribution of this paper is the synthesis of a constrained real-time trajectory planning approach for Autonomous Emergency Steering (AES) and its application in a demonstrator vehicle of the SAFE-UP project. The proposed concept consists of two core elements. The first element is a model-based trajectory planner which takes into account both state and input constraints. The second element is a sampling-based optimization algorithm that embeds the planner and performs an online optimization of AES trajectories. After the synthesis of the core elements, the resulting planner is discussed and demonstrated both in a simulation study and a vehicle testing campaign on a test track, where the algorithm is set up to perform a cyclic trajectory re-planning during an AES maneuver.

Keywords: Maritime, Predictive control for nonlinear systems, Feedback linearization
Abstract: Crane-based cargo handling at sea is subject to a variety of disturbances linked to the sea swell. During rough sea states, spatial payload oscillations occur, which render safe loading operations challenging. Therefore, increasing effort is made to automate disturbance rejection in terms of sway reduction (Anti Sway Control) and vertical position control (Active Heave Compensation). In this context, model predictive control (MPC) has been studied, which allows to consider both stabilization and tracking problems. In context of this contribution, a predictive controller based on the differential flatness of the crane system is presented, which facilitates real-time control while attaining a high damping performance with respect to the swinging payload. The controller design is evaluated in simulation for varying sea disturbances. The results suggest good tracking capabilities, where the payload's position error is reduced up to 85 % for light to medium sea states. In comparison to a nonlinear formulation of the MPC problem, computational complexity is furthermore reduced, enabling fast sample times around 10 ms.

Keywords: Nonlinear system theory, Algebraic/geometric methods, Feedback linearization
Abstract: We consider networks of two synaptically coupled excitatory-inhibitory neural modules with maximal symmetry of the connection strengths, and for which the nonlinearities are described by a logistic sigmoidal function. It has been shown that the connection strengths may slowly vary with respect to time and that they can actually be considered as inputs of the network. In the recent publication [Nicolau and Mounier, 2022], we considered the case of two synaptically coupled subnetworks and studied the problem of determining which connection strengths should be modified (in other words, which connection strengths should be considered as inputs), in order to achieve flatness for the resulting control system when no relation between the connection strengths (in particular, no symmetry) is assumed. In this paper, we consider a similar problem but under the assumption that all interactions (interactions between subnetworks and local interactions within the same subnetwork) are always symmetric.

Keywords: UAV's, Aerospace, Robotics
Abstract: Quadcopter trajectory tracking control has been extensively investigated and implemented in the past. Available controls mostly use the Euler angle standard to describe the quadcopter’s rotational kinematics and dynamics. As a result, the same rotation can be translated into different roll, pitch, and yaw angles because there are multiple Euler angle standards for characterisation of rotation in a 3-dimensional motion space. Additionally, it is computationally expensive to convert a quadcopter’s orientation to the associated roll, pitch, and yaw angles, which may make it difficult to track quick and aggressive trajectories. To address these issues, this paper will develop a flatness-based trajectory tracking control without using Euler angles. We assess and test the proposed control’s performance in the Gazebo simulation environment and contrast its functionality with the existing Mellinger controller, which has been widely adopted by the robotics and unmanned aerial system (UAS) communities.

Keywords: Predictive control for nonlinear systems, Lyapunov methods, Robotics
Abstract: This paper proposes a novel controller framework that provides trajectory tracking for an Aerial Manipulator (AM) while ensuring the safe operation of the system under unknown bounded disturbances. The AM considered here is a 2-DOF (degrees-of-freedom) manipulator rigidly attached to a UAV. Our proposed controller structure follows the conventional inner loop PID control for attitude dynamics and an outer loop controller for tracking a reference trajectory. The outer loop control is based on the Model Predictive Control (MPC) with constraints derived using the Barrier Lyapunov Function (BLF) for the safe operation of the AM. BLF-based constraints are proposed for two objectives, viz. 1) To avoid the AM from colliding with static obstacles like a rectangular wall, and 2) To maintain the end effector of the manipulator within the desired workspace. The proposed BLF ensures that the above-mentioned objectives are satisfied even in the presence of unknown bounded disturbances. The capabilities of the proposed controller are demonstrated through high-fidelity non-linear simulations with parameters derived from a real laboratory scale AM.

Keywords: Model/Controller reduction, Reduced order modeling, Nonlinear system theory
Abstract: We present a recently developed method for model reduc- tion of nonlinear systems based on newly defined Gramians for the variational system. Within the contraction framework, global properties can obtained for the reduced order nonlin- ear systems. Computational considerations lead to sampling based Gramian approximations, that provide a way to com- pute the Gramians from data, and thus provide a way to compute the balanced realization based on which a reduced order nonlinear model can be obtained.

Keywords: Model/Controller reduction, Reduced order modeling, Nonlinear system theory
Abstract: We present a method to calculate a low order model of a linear system of large dimension, that matches a set of high order moments of the transfer function and achieves pole-zero placement constraints as well as derivative constraints. The model satisfying all the constraints simultaneously is selected from a family of parametrized reduced order mod- els. The parameters are computed solving an explicit, but simple linear algebraic system. Furthermore, we make the connection to the Loewner framework. To this end, we construct the Loewner matrices from the given data and the imposed pole-zero and derivative moment constraints. The resulting approximations achieve a trade-off between good norm approximation and the preservation of the dynamics of the given system in a region of interest. We illustrate the results on the academic example of a cart controlled by a double pendulum and the practical example of a CD player.

Keywords: Model/Controller reduction, Reduced order modeling, Nonlinear system theory
Abstract: Model reduction in the Loewner framework is an interpolation-based approach aimed at achieving model order reduction. The backbone of this approach is the Loewner matrix. It is a divided-difference matrix built from tangential interpolation data. The Loewner matrix is independent of any particular state-space representation since tangential data are acquired by sampling a transfer matrix, causing the loss of the underlying system’s structure for arbitrary interpolation points. In light of this phenomenon, we intend to delve into the selection of tangential interpolation data used to construct reduced-order models. We show that the high-order model must be interpolated at particular frequencies and along particular directions in order to retain the underlying system’s structure. The discussion focuses on the assignment and preservation of particular structures in the interpolant, such as port-Hamiltonian systems, second-order equations, and network systems on graphs.

Keywords: Model/Controller reduction, Reduced order modeling, Nonlinear system theory
Abstract: A new family of systems matching sets of tangential data is presented in the Loewner framework. The family is constructed by dynamic extension of the classically given interpolant, performed in such a way that the resulting family of systems still matches the data. Rather than simply adding more interpolation points, performing the extension in this way gives the designer much more control over the structure of the resulting interpolant. The new approach allows for the assignment of stability and other structural properties for a reduced order model in scenarios where it would otherwise be difficult to accomplish.