Keywords: Control Technology

Keywords: Adaptive control
Abstract: This paper presents a continuous-time output feedback adaptive control technique for stabilization and tracking control problems. The adaptive controller is motivated by the classical discrete-time retrospective cost adaptive control algorithm. The particle swarm optimization framework automates the adaptive algorithm’s hyperparameter tuning. The proposed controller is numerically validated in the tracking problems of a double integrator and a bicopter system and is experimentally validated in an attitude stabilization problem. Numerical and experimental results show that the proposed controller is an effective technique for model-free output feedback control.

Keywords: Adaptive control, Optimization, Simulation
Abstract: Wave energy is a promising renewable resource, yet traditional wave energy conversion (WEC) systems suffer from limitations due to their stationary deployment. In particular, stationary WECs require several months or even years for permitting and installation, and they cannot be relocated to suit evolving demands after their installation. While these limitations are not necessarily an issue for long-term deployments, they are problematic for applications such as disaster recovery or temporary power supplementation in island communities, which require rapid deployability. This work examines a mobile wave glider system that combines the principles of a rapidly deployable wave glider with an auxiliary power take-off (PTO) system that utilizes a fraction of the available wave power to charge an on-board battery through an active damper. The contribution of this work lies in a real-time power optimization scheme that combines a sea-state-driven, model-based lookup table (based on a customized model developed by the authors) with extremum seeking control (ESC) to learn a correction to the lookup table. This approach is based on the observation that the optimal corrections will exhibit limited, slow variation relative to the variations in the sea state itself. Simulations, driven by wave data from the island nation of Palau and a scaled model of the system, demonstrate the effectiveness of the proposed control strategy in achieving near-optimal energy harvesting.

Keywords: Mechatronic systems, Adaptive control, Control applications
Abstract: We present a model‑free, data‑driven state‑derivative feedback controller for active magnetic levitation. An online policy‑iteration scheme updates the control law from real‑time state and input data, directly minimizing a quadratic cost while guaranteeing closed‑loop stability. Training alternates between data collection and policy updates over several epochs to reduce bias, and the best policy is selected at the end. Hardware tests confirm robust performance despite dynamic and equilibrium uncertainties.

Keywords: Adaptive control, Complex systems, Neural networks
Abstract: Controlling the complex combustion dynamics within solid fuel ramjets (SFRJs) remains a critical challenge limiting deployment at scale. This paper proposes the use of a neural network model to process in-situ measurements for monitoring and regulating SFRJ thrust with a learning-based adaptive controller. A neural network is trained to estimate thrust from synthetic data generated by a feed-forward quasi-one-dimensional SFRJ model with variable inlet control. An online learning controller based on retrospective cost optimization is integrated with the quasi-one-dimensional SFRJ model to regulate the thrust. Sensitivity studies are conducted on both the neural network and adaptive controller to identify optimal hyperparameters. Numerical simulation results indicate that the combined neural network and learning control framework can effectively regulate the thrust produced by the SFRJ model using limited in-situ data.

Keywords: Energy Storage, Energy Systems, Renewable Energy
Abstract: Maintaining grid balance represents a significant challenge in light of the growing proportion of renewable energy sources in power generation. A viable solution to enhance grid balance is the implementation of Battery Energy Storage Systems (BESS), which can be charged during low-demand periods and discharged during peak load times. This paper investigates the feasibility of deploying a BESS within a district comprising a population of 100,000. The proposed optimization algorithm is formulated as a mixed-integer linear programming problem. While this algorithm applies a linear battery model, the results are compared to those obtained from a second-order equivalent circuit model. The findings indicate that the dynamic operation of the BESS can effectively reduce the standard deviation of the residual load by as much as 3.5 MWh.

Keywords: Energy Systems, Energy Storage, Control applications
Abstract: This article investigates real-time fast charging for lithium-ion batteries at high C-rates. First, an electrochemicalthermal- inspired battery model is presented to capture key dynamics accurately. The model is validated through hardware experiments, showing small modeling errors across a wide range of charging (up to 4 C) and discharging (up to 14.5 C) currents. A fast-charging framework is then proposed, consisting of two components: an offline trajectory optimization and an online current reshaping algorithm. First, the offline component solves a time-optimal charging trajectory optimization problem once at the start of the charging process. This generates an optimal reference trajectory for battery states and controls, which is then used by the online component. Second, the online component continually reshapes the reference charging current in real time. Operating at a higher frequency, it adjusts the current based on the present state and the reference current for the next time instance, ensuring compliance with charging constraints. Numerical experiments confirm the effectiveness and computational efficiency of the proposed framework.

Keywords: Energy Storage, Modeling, Distributed parameter systems
Abstract: Lithium-ion batteries have grown larger in recent years in response to the need to increase pack-level energy densities. One of the main issues that emerges in these large format cells are heterogeneities. In particular, temperature hotspots in large pouch cells have been shown to influence degradation rates and fast charging limits. Motivated by these issues, this paper proposes computationally efficient models and bounds to characterise thermal hotspots in pouch cells. The models are validated against experimental data and shown to capture the spread in temperature across the pouch cell. The bounds exploit external positivity of the underlying system to improve scalability and are shown to be tight under constant current cycling. Through these results, bounds for the maximum temperature a pouch cell will experience during an arbitrary use-profile can be computed at scale and so be used to support battery safety and control.

Keywords: Control applications, Estimation, Linear robust control
Abstract: This paper proposes a robust observer for temper- ature estimation in All-Solid-State Batteries (ASSBs) based on a coupled electrochemical-thermal model. The thermal subsystem is modeled using a quintuple-state structure that captures the temperature dynamics of five key components: the cathode, electrolyte, anode, and the surfaces near the cathode and anode. The thermal model is driven by electrochemical heat generation, which is derived from a physics-based electrochemical model, in which the cathode diffusion coefficient depends on the cathode temperature. To ensure robustness under modeling uncertainties, the observer gain is designed such that the estimation error dynamics achieve D-stability, constraining the observer poles to lie within a prescribed disk in the complex plane. For simulation purpose, a piecewise time-discretized implementation of the coupled model is adopted. Simulation results under the Urban Dynamometer Driving Schedule (UDDS) demonstrate the accuracy of the proposed temperature observer and its robustness to parameter uncertainties, demonstrating its potential for reliable thermal monitoring and enhanced safety in ASSBs.

Keywords: Automotive applications
Abstract: Battery electric vehicles (BEVs) are an important contribution to emission-free mobility for private transportation. The thermal management of a BEV regulates the temperatures of its components to maintain a desired performance.

This work examines the impact of the cooling circuit layout on the energy efficiency of the thermal management while ensuring compliance with temperature limits of the components. To achieve the desired performance, an optimal control problem (OCP) is formulated to govern the control inputs and is further extended to jointly optimize the cooling circuit layout.

This OCP is discretized using direct collocation with the implicit midpoint rule and solved by an interior point method.

Simulations under different ambient conditions demonstrate improved efficiency in the usage of the control inputs while maintaining temperature constraints through the optimization of the cooling circuit layout.

Keywords: Automotive applications, Observers
Abstract: Electrochemical sensors have been broadly applied in various areas for diagnostics and controls. However, many electrochemical sensors are often cross-sensitive to one or more interfering compounds, which can make it rather challenging in attaining accurate concentration of the target compound. The sensor cross-sensitivity can cause instability issues in feedback control systems and inaccurate diagnostics. In many cases, a state estimation problem with cross-sensitive output is equivalent to a state estimation problem with limited number of state candidates that can lead to the same output. In this study, an innovative estimation error-based observer is proposed for state estimation for a general class of 1st-order nonlinear dynamic systems with cross-sensitive output. By constructing observers for each potential candidate and analyzing the respective estimation errors, the true state (or mode) of the system can be determined based on the cross-sensitive output measurement. The proposed algorithm was applied to estimate the ammonia coverage ratio of a Diesel selective catalytic reduction system with ammonia cross-sensitive NOx sensor output. Simulation results show that the true ammonia coverage ratio can be accurately estimated with this method at both low and high ammonia coverage ratios. This method can be applied to a broad class of nonlinear dynamic systems with cross-sensitive outputs for state estimation and decoupling the target compound from the interfering compounds in the sensor readings.

Keywords: Embedded systems, Real-time systems, Computer-aided control design
Abstract: The manufacture of the most advanced semiconductor chips requires the capability to produce ever smaller features, of which the EUV source made by ASML is a key enabler. To etch such features, the amount of variation in EUV photons that reaches the wafers must be controlled within stringent bounds. The generation of EUV is a multistage process occurring at extreme conditions, with many stages regulated by individual control systems which generally lack a common reference and have limited communication with one another. How these systems operate together and how they contribute to the final performance in wafer exposure is not trivial. Our goal here is to provide the reader a sense of the objectives, interactions, and relative scale of several critical control systems, which are described in other papers by authors at ASML, in the context of the overall EUV source.

Keywords: Control applications, Process control, Machine learning
Abstract: Excimer lasers are a critical component of modern lithography, with fine light beam quality (in terms of wavelength, line width, and energy) of extreme importance for wafer etching accuracy. However existing works in this field concentrate on control strategies for these beam parameters. In this paper, we consider a suite of adaptive algorithms called ADAPT that work over these lower-level control strategies to optimize laser performance as well as ensure higher laser availability and energy savings for the chipmakers. In particular, we consider three diverse algorithms including smart Gas Life Extension (sGLX), smart Gas Temperature Control (sGTC), and smart Absolute Wavelength Reference (sAWR) that dynamically change reference parameters for the lower-level controllers without sacrificing productivity. We study the exact concept and design of these adaptive algorithms and the corresponding savings generated.

Keywords: Predictive control, Optimization, Nonlinear systems
Abstract: Nonlinear Model Predictive Control (NMPC) is a powerful and versatile controller scheme, whose computational demands often lead to control delays that are unaccounted for in analysis. Fast NMPC schemes such as Advanced Step NMPC (asNMPC) and Real-Time Iterations (RTI) attempt to compensate by respectively removing control delay and minimizing sampling time. This paper introduces Advanced Real-Time Iterations (aRTI), combining concepts from asNMPC and RTI in order to take advantage of both of their strengths. In this way, aRTI can achieve very small control delays, while attaining very fast control rates. Additionally, we propose the Short-Horizon predictor (SH-predictor) to lower the computational demands of preparing a tangential predictor. Simulations of a perturbed `pendulum on cart' system demonstrate the effectiveness of aRTI and SH-predictor in achieving high control performance, reducing computational loads, and minimizing control delay.

Keywords: Predictive control, Power Electronics, Nonlinear systems
Abstract: In various power electronic converters bulky capacitors are used as passive energy buffers on the dc bus. In order to reduce the capacitance, some solutions have been proposed to replace these with active energy buffering circuits. These active buffering circuits, or active dc-links, are nonlinear systems. This poses a challenge in applications which must buffer highly transient currents composed of many different frequencies. This paper presents a nonlinear model predictive (NMPC) control scheme that makes use of state and disturbance predictions to accurately control an active dc-link system to provide better buffering capability and provide integrated constraint handling. Simulation of the NMPC control scheme and active dc-link system show the effectiveness of the control scheme and some potential for further capacitance reduction.

Keywords: Predictive control, Machine learning, Iterative learning control
Abstract: This work proposes a data-driven economic model predictive control formulation with tracking features for changing economic targets. An adaptive tuning mechanism, integrated into the control system, dynamically adjusts the controller tuning parameters based on changes of economic targets. The adaptation process leverages inference models built with machine learning techniques. This approach enables the controller to adapt to the current conditions of the system, generating a transient behavior that enhances the overall economic performance. A case study is discussed to corroborate the benefits and properties of the proposed controller.

Keywords: Process control, Linear robust control, PID control
Abstract: Modern heat pumps operate over increasingly wide ranges of temperatures, heat loads, compressor and fan speeds, making robustness of feedback controllers an increasingly critical issue. This paper presents results of a model-based analysis of process gain variation, and identifies a relatively simple approach for scheduling compensator gains for a conventional heat pump feedback architecture. The approach is effective at addressing process gain variation caused by widely ranging values of compressor and fan speeds. To address potential loss of feedback control robustness caused by plant uncertainty that is unmeasured, typically due to heat pump installation and application variability, the theory of unfalsified control is studied. An unfalsified control algorithm is designed for one feedback loop that is particularly susceptible to unmeasured plant uncertainty, and shown to be functional in a nonlinear simulation. Several potential issues associated with maturing unfalsified control theory into a practical technology for application to production heat pumps are discussed.

Keywords: Estimation, Mechatronic systems, Kalman filtering
Abstract: Most modern robotic joints are equipped with strain wave gears (SWG) as well as measurement devices like encoders and torque transducers. Torque information can also be gained without torque transducers from strain gauge sensors that are mounted on the flex spline, the deformable part of SWGs. These sensor signals provide, in addition, information about the input rotation speed and can therefore be exploited for its estimation. This paper focuses on this input rotation speed estimation by means of a peak-to-peak scheme, a trigonometric function approach by utilizing an alternative sensor setup, an extended Kalman filter (EKF), and a frequency-locked loop. The analysis lead to the conclusion that the EKF shows the highest potential to accomplish this task.

Keywords: Estimation, Sensor fusion, Filtering
Abstract: Using Bayesian transfer learning, we develop a particle filter approach for tracking a nonlinear dynamical motion model in a dual-sensor system where intensities of measurement noise for both sensors are asymmetric. The densities for Bayesian transfer learning are approximated with the sum of weighted particles to improve the tracking performance of the primary sensor, which experiences higher noise intensity compared to the source sensor. Simulation results are presented that validate the effectiveness of the proposed approach compared to an isolated particle filter and transfer learning applied to the unscented Kalman filter and the cubature Kalman filter.

Keywords: Localization, Sensor fusion, Sensors
Abstract: Determining the orientation of a rigid body using an inertial measurement unit is a common problem in many engineering applications. However, sensor fusion algorithms suffer from performance loss when other motions besides the gravitational acceleration affect the accelerometer. In this paper, we show that linear accelerations caused by rotational accelerations lead to additional zeros in the linearized transfer functions, which are strongly dependent on the operating point. These zeros lead to non-minimum phase systems, which are known to be challenging to control. In addition, we demonstrate how Mahony and Madgwick filters can mitigate the effects of the additional acceleration, but at the cost of reduced bandwidth. This generates insights into a fundamental problem in estimation, that are transferable to many practical applications.

Keywords: Estimation, Linear systems, Automotive applications
Abstract: Motivated by a crash that occurred due to a sensor malfunction in the Indy Autonomous Challenge, this article takes a secure estimation-inspired approach to sensor protection and fault detection and applies it to path following for autonomous vehicles. We introduce the notion of sensor protection and then use this notion to formulate an Optimal Sensor Protection Problem. We adapt existing algorithms for secure estimation to the protected sensor paradigm. Finally, these ideas are applied to a nonlinear model of a car-like robot with realistic model parameters and provide extensive numerical simulations to test the efficacy of this approach on nonlinear models.

Keywords: Biologically-inspired methods, Vision, Mapping
Abstract: An event-based camera is a biologically-inspired vision sensor that captures changes in a scene by recording pixel-level events and delivering asynchronous event streams. Event-based cameras excel in capturing fast-moving scenes and high-dynamic range environments, scenarios where conventional cameras often struggle. These characteristics make them well-suited for advanced robotics and computer vision tasks. This paper introduces EVO-RatSLAM: a pioneering biologically-inspired SLAM technique specifically designed for computing visual odometry with event-based cameras. This is the first SLAM approach that utilizes event-based visual odometry to create consistent and robust maps of camera motion over large spatial areas and for extended periods of time. This method, inspired by the computational model of rodents' hippocampus, combines continuous attractor network models with event-based visual inputs to construct cognitive maps. Odometric information is obtained by aligning the events using a probabilistic model. We validate the performance of EVO-RatSLAM through multiple real-world datasets captured using the DVXplorer event-based camera in various scenarios.

Keywords: Identification, Estimation, Transportation systems
Abstract: This paper presents a novel approach for tire stiffness estimation in vehicle dynamics using Differentiable Moving Horizon Estimation (DMHE). The method leverages the OptNet framework, which enables embedding convex optimization as a differentiable layer, allowing gradient-based learning of physical parameters. Unlike traditional Moving Horizon Estimation (MHE) or the Dual Extended Kalman Filter (DEKF), the proposed DMHE algorithm jointly estimates vehicle states and unknown stiffness parameters by backpropagating through the optimization layer. The approach is validated using simulations under varying initial conditions and noise, showing faster convergence and improved accuracy. While lateral velocity is assumed measurable—consistent with standard IMU-based vehicle sensing practices—the framework is extensible to observer-based architectures. Additionally, the paper discusses how DMHE can be generalized to handle time-varying stiffness, thereby enhancing its applicability to real-world driving scenarios where tire properties evolve over time. The results suggest DMHE is a promising tool for robust, real-time parameter identification in intelligent vehicle control systems.

Keywords: Time delays, Linear robust control, Filtering
Abstract: This paper presents a comprehensive analysis and design of time-delay filters for systems with nonzero initial conditions, focusing on robustness in the presence of initial positions and velocities. A novel approach is introduced to account for nonzero initial conditions by deriving a closed-form representation of the time-delay filters using Weierstrass substitution in the frequency domain, enabling precise and efficient implementation. The proposed methodology is validated through numerical examples, including a single spring-mass system and a velocity-driven gantry crane payload model, where the robust algorithm is applied to the latter and demonstrates the effectiveness of the filter. This work provides new insights into the role of initial conditions in time-delay filters design for their practical application in systems requiring high precision and robustness.

Keywords: Distributed parameter systems, Variational methods, Modeling
Abstract: A finite-dimensional nonlinear model for the heavy rope with a freely moving suspension point is presented using energy-based discretization. The derivation proceeds from the Lagrangian to derive the lumped model by discretizing the Lagrange density and applying the principle of least action. The discretized model is simulated at multiple resolutions, incorporating flatness-based motion planning schemes. The planned trajectory is executed on an industrial robot, with a heavy rope attached to its end-effector. Using camera-based offline tracking of the rope's free end, comparisons are conducted between the simulation model and the planned trajectory, as well as between the measurements and the planned trajectory.

Keywords: Autonomous systems, Planning, Optimization
Abstract: We address the problem of minimum time intercept of multiple mobile targets that are translating in a fixed direction, that is, moving with identical constant speeds in the same direction. Every target must be intercepted by a heterogeneous pursuer assembly} -- a mobile vehicle carrying multiple identical pursuers, {each capable of moving faster than the vehicle. Aside from this novel problem formulation, our main contributions are as follows. First, we formally establish that the optimal heterogeneous intercept problem is equivalent to solving an appropriately defined Euclidean minimum Hamiltonian path through neighborhoods (EMHP-N) problem, where each neighborhood is an ellipse characterized by its center and the lengths of its major and minor axes. Second, we derive novel upper bounds on the optimal length as a function of the problem parameters, such as the number of targets, the speed ratios between the target, the pursuer and the assembly, the geometry of the region containing the targets and the time required for the pursuer to intercept a target. {Notably, our derived bounds on path length do not require prior knowledge of the targets' current exact locations}. Finally, we offer insight into the approach through a numerical visualization and discuss possible improvements to the upper bounds.

Keywords: Mechanical systems, Robotics applications, Nonlinear systems
Abstract: The problem of realizing rotary propeller motion of a devil-stick in the vertical plane using forces purely normal to the stick is considered. This problem represents a nonprehensile manipulation task of an underactuated system. In contrast with previous approaches, the devil-stick is manipulated by controlling the normal force and its point of application. Virtual holonomic constraints are used to design the trajectory of the center-of-mass of the devil-stick in terms of its orientation angle, and conditions for stable propeller motion are derived. Intermittent large-amplitude forces are used to asymptotically stabilize a desired propeller motion. Simulations demonstrate the efficacy of the approach in realizing stable propeller motion without loss of contact between the actuator and devil-stick.

Keywords: Identification, Estimation, Modeling
Abstract: This paper addresses trajectory optimization for the excitation of systems with uncertain parameters, subject to input power constraints. Since the input required to follow a given trajectory depends on the system parameters which are to be estimated, this task is non-trivial. Therefore, we present worst-case and chance constraints to limit the short-time average power demand and optimize the excitation trajectory for energy-based parameter estimation. The effectiveness of our approach is verified through extensive simulation experiments. The results demonstrate that the optimized trajectories can be followed without violating power constraints and yield better parameter estimates compared to randomly selected trajectories, both under low and high power limits.

Keywords: Optimization, Complex networks, Control applications
Abstract: In this paper we consider a Deterministic Annealing (DA) framework for optimizing reservoir placement and associated water transport costs in Water Distribution Networks (WDNs). We introduce a capacity-constrained formulation to ensure that each junction in the network serves a limited number of demand points, preventing overload and improving system stability. Moreover, we propose update rules for secondary Lagrange multipliers corresponding to capacity inequality constraints. Finally, we extend the framework to utilize real-world pipe networks and validate our algorithm through a case study on the Modena, Italy network. Experimental results demonstrate that our DA-based method achieves lower operational costs compared to previous approach.

Keywords: Control Technology

Keywords: Robotics, Nonlinear robust control
Abstract: This paper presents a robust enhancement to task-priority control methodologies for redundant robotic systems, specifically addressing vehicle-manipulator systems (VMS). A novel control law is proposed, which ensures global exponential stability of the tracking errors in non-singular configurations while providing robust performance under bounded disturbances. Additionally, we establish new, stronger stability guarantees for existing methods. The efficacy of the proposed approach is validated through simulations and a comparative study on a 7-DoF Franka Emika robot manipulator, highlighting the performance of the different control approaches in trajectory tracking and robustness. These advancements contribute to the development of robust, autonomous, and energy-efficient robotic systems for complex applications, such as underwater vehicle-manipulator systems (UVMS), enabling safer and more cost-effective operations.

Keywords: Marine/underwater robotics, Learning, Nonlinear systems
Abstract: This paper presents a safety-aware deep reinforcement learning (DRL)-based trajectory tracking control of autonomous surface vessels (ASVs). A multilayer neural network (MNN) observer estimates the ASV's state and uncertain dynamics. By utilizing the estimate state vector from the observer, a safety-aware DRL-based optimal policy is formulated using control barrier functions (CBF) and Karush-Kuhn-Tucker (KKT) conditions. An actor-critic MNN with singular value decomposition (SVD)-based updates mitigates vanishing gradients. To enhance adaptability, an online safe lifelong learning (SLL) scheme counters catastrophic forgetting across varying ASV dynamics. The Shapley Additive Explanations (SHAP) method identifies key features influencing the control policy. Simulations on an underactuated ASV show that SLL-based control reduces cumulative costs by 17% and RMS error by 32% compared to a baseline without SLL.

Keywords: Aerial robotics, Autonomous systems, Reinforcement learning
Abstract: This article considers the infinite time horizon optimal tracking control problem for discrete-time (DT) partially uncertain strict feedback systems with application to quadrotor UAVs. First, the strict feedback DT system is transformed into an equivalent affine nonlinear DT system in terms of tracking error dynamics. The optimal tracking control problem is solved using an augmented system approach, where a horizon of future reference trajectory points are used in the augmented state, as compared to using a single point. The internal dynamics of the original nonlinear strict feedback system and the transformed affine system in terms of error dynamics are considered unknown whereas the control coefficient matrix of the affine system is considered known. By applying approximate dynamic programming (ADP) using multilayer neural networks (MNNs), the optimal control policy is obtained. Under our proposed MNN weight update laws, the tracking and weight estimation errors are proven to be uniformly ultimately bounded (UUB) using Lyapunov analysis. The efficacy and reliability of the method are demonstrated through hardware implementation on the Quanser QDrone2 UAV.

Keywords: Planning, Mobile Robots, Robotics
Abstract: This paper addresses the constrained navigation problem for autonomous robots in unknown, cluttered environments, emphasizing safety during online operations. A networked control framework is developed using model predictive control and a set-theoretic approach. The proposed architecture ensures anti-collision capabilities despite communication delays and mission success despite unpredictable obstacles. Formal proofs establish trajectory constraints and uniform boundedness under vehicle uncertainties. The study focuses on skid-steered tracked mobile robots, valued for their flexibility and adaptability in hazardous scenarios. Experiments validate the architecture's effectiveness, demonstrating its advantages in collision avoidance, trajectory regulation, and robust performance in complex, dynamic settings.

Keywords: PID control, Estimation, Neural networks
Abstract: We propose an output feedback self-tuning PID control approach that leverages Wavelet Neural Network (WNN) for online model identification and real-time trajectory tracking of Unmanned Aircraft Systems (UASs). In contrast to conventional neural network (NN)-based nonlinear dynamic modeling, which approximates nonlinearities using a superposition of sigmoidal functions, our method employs wavelet functions as universal approximators. The input-output dynamics identification is performed using discrete adaptive wavelets as activation functions. The WNN scheme preserves spatiotemporal information, a feature unavailable when adopting classic NN architectures. The contribution of the proposed control strategy has two major components: (i) the output identification of a fast and agile UAS, and (ii) the exploitation of such knowledge to implement the self-tuning of the model-free PID control gains. Experimental results on real-time trajectory tracking are presented to demonstrate the effectiveness of the proposed approach.

Keywords: Optimization, Nonlinear systems, Modeling
Abstract: A control optimization approach is presented for a chaser spacecraft tasked with maintaining proximity to a target space object while avoiding collisions. The target object trajectory is provided numerically to account for both passive debris and actively maneuvering spacecraft. Thrusting actions for the chaser object are modeled as discrete (on/off) variables to optimize resources (e.g., fuel) while satisfying spatial, dynamical, and collision-avoidance constraints. The nonlinear equation of motion is discretized directly using a fourth-order Runge-Kutta method without the need for linearized dynamics. The resulting mixed-integer nonlinear programming (MINLP) formulation is further enhanced with scaling techniques, valid constraints based on a perspective convex reformulation, and a combination of continuous relaxations of discrete actions with rounding heuristics to recover high-quality feasible solutions. This methodology enables efficient, collision-free trajectory planning over extended time horizons while reducing computational overhead. The effectiveness and practicality of the proposed approach is validated through a numerical case study.

Keywords: Transportation systems, Predictive control, Nonlinear systems
Abstract: Koopman Model Predictive Control (KMPC) and Data-EnablEd Predictive Control (DeePC) use linear models to approximate nonlinear systems and integrate them with predictive control. Both approaches have recently demonstrated promising performance in controlling Connected and Autonomous Vehicles (CAVs) in mixed traffic. However, selecting appropriate lifting functions for the Koopman operator in KMPC is challenging, while the data-driven representation from Willems’ fundamental lemma in DeePC must be updated to approximate the local linearization when the equilibrium traffic state changes. In this paper, we propose a dictionary-free Koopman model predictive control (DF-KMPC) for CAV control. In particular, we first introduce a behavioral perspective to identify the optimal dictionary-free Koopman linear model. We then utilize an iterative algorithm to compute a data-driven approximation of the dictionary-free Koopman representation. Integrating this data-driven linear representation with predictive control leads to our DF-KMPC, which eliminates the need to select lifting functions and update the traffic equilibrium state. Nonlinear traffic simulations show that DF-KMPC effectively mitigates traffic waves and improves tracking performance.

Keywords: Predictive control, Control applications, Transportation systems
Abstract: Data-driven predictive control has gained attention as a model-free alternative, particularly when obtaining an explicit system model is challenging or costly. However, its application to mixed traffic systems requires data sharing, raising significant concerns about privacy leakage. This paper presents a novel masking-based strategy to enable privacy-preserving data-driven predictive control in mixed traffic environments, where connected and automated vehicles (CAVs) operate alongside human-driven vehicles (HDVs). The proposed method protects against external eavesdroppers who intercept communication channels in an attempt to infer the states and control inputs of CAVs. By integrating an affine transformation with random noise injection, our strategy obfuscates actual system states and inputs, leading to a modified data-enabled predictive control framework that ensures both privacy protection and optimal control. We demonstrate that this method can achieve differential privacy without degrading control performance or imposing heavy computational burdens. Simulation results validate the effectiveness of the proposed approach.

Keywords: Transportation systems, Control applications, Control Technology
Abstract: The introduction of automated vehicles (AVs) is expected to transform traffic dynamics significantly, necessitating new control strategies. Although AVs have potential benefits in terms of improved traffic flow, early versions of AVs such as adaptive cruise control (ACC) vehicles may have disruptive impacts on traffic flow, which motivates the development of new traffic control strategies. While prior work has demonstrated that an extended ramp metering control algorithm is efficient in regulating traffic at different market penetration rates of automated vehicles in theory. However, it remains unclear whether the proposed control algorithm is effective for implementation. In this study, we analyze how mixed-autonomy traffic affects the performance of ramp metering by simulating the control algorithm on a Minneapolis on-ramp site. Our simulation results show that a higher market penetration rate of ACC vehicles can diminish traffic flow efficiency, while AVs show potential improvements in traffic flow dynamics. Furthermore, we propose an adjusted algorithm that improves mainline traffic flow conditions while significantly reducing ramp waiting times in different mixed autonomy traffic scenarios. The findings of this study offer insights into adaptable traffic management strategies for mixed-autonomy traffic.

Keywords: Automotive applications, Transportation systems, Reduced order modeling
Abstract: This paper proposes a safety-critical connected cruise control strategy using backstepping control barrier functions to enforce safety for connected automated vehicles while satisfying actuator limits. The proposed approach accounts for the vehicle’s response time, modeled as a first-order lag. We investigate the impact of braking limits and lag time on the conservativeness of the safe region in the state space. Using simulations, we confirm that the proposed controller ensures safety while maintaining feasibility.

Keywords: Transportation systems, Control architectures, Game theory
Abstract: This study proposes a hierarchical control of CAVs (Connected and Automated Vehicles) in a mixed traffic scenario. Although there have been many studies using CAVs, there are still few studies on mixed traffic in which a HDV (Human Driven Vehicle) and CAVs coexist. Focusing on the merging control of mixed traffic scenario, many studies have fixed the merging point and merging order, which should be able to be determined freely. To address this problem, we propose a 3-layer hierarchical control method. The upper layer uses a coalitional game based on level-K reasoning. The middle layer uses model predictive control. Finally, the lower layer uses control barrier function. The upper layer decides when to merge in an area by considering the behavior of HDV. The middle layer generates trajectories within constraints. The lower layer uses elliptical constraints to follow trajectories under free merging order safely. These are operated at different time steps. Simulation verification of highway merging is performed to demonstrate the effectiveness of the proposed method.

Keywords: Optimization, Complex networks, Modeling
Abstract: In this paper, we study the scenario that connected and automated vehicles (or CAVs) cross a multi-lane intersection, where the intersection is divided into multiple spaces for vehicles to travel through. The divided spaces within an intersection are the resources that CAVs compete for. For each CAV, there is a fixed sequence of the resources that it must follow without interruption. To model the nonlinear dynamics of timing evolution and solve the scheduling and control co-design problem in the multi-lane intersection, we establish a new analytical timing model for such non-preemptive multi-resource and multi-task with fixed occupation order. The contention-resolving model predictive control (or MPC) is also modified to incorporate the more complicated timing model, such that all CAVs can drive through the intersection with minimal total time delay. The illustrative example in the simulation is provided to verify the effectiveness of our approach comparing with the first-come-first-serve (or FCFS) scheduling strategy.

Keywords: Computational methods, Cyberphysical systems, Discrete event systems
Abstract: Current-state opacity deals with the capability to hide a set of reachable states from a malicious intruder that can partially observe the system’s dynamic. In bounded discrete event systems, the main approach adopted to verify opacity is the so-called observer-based one, where a (possibly reduced order) observer or estimator of the system’s dynamic is used to verify whether the intruder can infer if the estimated current state is fully included in the set of secrets. On the other hand, sequence-based approaches to verify opacity consist of checking if, given a sequence leading to the secret state, there exists at least another one generating the same external observation but leading to a not-secret state, on a language with finite-cardinality, even in the case of live systems. This paper compares the performance of two different algorithms for current-state opacity verification: a sequence-based one that requires solving optimization problems and observer-based ones that use a modified version of the Basis Reachability Graph.

Keywords: Discrete event systems, Optimization, Cybersecurity
Abstract: This paper addresses the diagnosability analysis problem under malicious attacks of a discrete event system modeled by labeled Petri net. We focus on a stealthy re- placement attack to alter or corrupt the observation of the system.The aim of this work is, from an attacker viewpoint, to design a stealthy replacement attack for violating the diagnos- ability of system. To this end, we first build a new structure, called attack verifier that is used to enumerate all the attack paths. Then an optimal attack synthesis problem in terms of minimum energy cost is formulated by determining whether a bad path is generated via solving a set of integer linear programming problems. Finally, an automated manufacturing system is provided to illustrate the proposed attack strategy.

Keywords: Discrete event systems, Control architectures, Manufacturing systems
Abstract: This study focuses on developing a control mechanism to prevent deadlocks in a specific subclass of Petri Nets. The designed control guarantees deadlock-freeness for every acceptable initial marking of the net. The novelty of this method lies in the removal of some output transitions of siphons by establishing a specific synchronic distance between the output and internal transitions of the siphons. This approach is shown in S3PR nets. An example is presented to illustrate the proposed approach.

Keywords: Discrete event systems
Abstract: Model predictive control is widely used in the process industry today due to its ability to handle constraints and multiple objectives. However, its application in fields where a discrete event system model is used, such as in manufacturing and logistics, remains limited. The state explosion problem represents a significant obstacle, which can be mitigated using a basis marking representation of the Petri net reachability space. Specifically, the transition set of a Petri net is partitioned into subsets of explicit and implicit transitions. Controllable transitions are all included in the explicit transition set. The firing of implicit transitions can be abstracted, allowing the reachability set of the net to be completely characterized by a subset of reachable markings called basis markings. In this preliminary paper, based on the receding horizon approach, it is proposed to compute future supervisory control actions by considering the basis markings reached by firing explicit transitions along sequences of length equal to the prediction horizon specified by a positive integer. A proper choice of prediction horizon is necessary to ensure closed-loop deadlock-freeness, consistency, and satisfaction of safety constraints.

Keywords: Optimization, Robotics applications, Planning
Abstract: The throughput of commercial packaging systems is inherently constrained to approximately two picks per second per picker due to fundamental physical limitations. This paper presents a novel rotary packaging system that leverages optimized scheduling and path planning algorithms to overcome these constraints. Initially, an overview of the mechanical design is provided and the notation to define the problem setting is given. Based on this, the scheduling and path planning algorithms are systematically derived. Each pick and place process is decomposed into five distinct phases, alternating between constrained optimal paths and belt synchronization, respectively. Direct collocation is employed to compute the optimal paths. The proposed approach is validated through numerical simulations and experimental testing on a physical demonstrator. Finally, potential directions for further performance improvements and system enhancements are discussed.

Keywords: Optimization
Abstract: Scheduling educational institution courses for multiple departments presents a significant challenge due to the complexity of constraints and requirements. This paper addresses the University Course Scheduling Problem (UCSP) using a binary Integer Linear Programming (ILP) approach to generate semester schedules for winter or summer semesters. The approach incorporates both hard constraints - such as avoiding simultaneous sessions for the same semester, adhering to room capacities, and ensuring lecturers are not doublebooked - and soft constraints, including preferences for lecture times. While the whole methodology integrates tools for data management, optimization modeling, and a structured workflow to create detailed schedules for lecturers, students, and room allocations, this paper focuses on the ILP-formulation of these constraints. The construction of these matrices is systematically explained through practical examples, illustrating how the relationships between courses, lecturers, rooms, and time slots are encoded. To validate the proposed approach, a case study was conducted at the Niederrhein University of Applied Sciences. This study addresses the scheduling requirements of three engineering departments with more than 50 degree programs and 260 modules, demonstrating the applicability of the matrixdriven ILP method.

Keywords: Manufacturing systems, Modeling
Abstract: Due to increasing awareness of sustainability management, circular economy practices such as remanufacturing become more relevant in the industrial landscape. Remanufacturing depicts an industrial process that reconditions old parts to a like-new condition, and thus retains economical value and conserves natural resources. Despite acknowledged operational complications, many remanufacturing enterprises neglect production planning and control activities, which leads to reduced productivity and efficiency. This, in turn, hinders and reduces the willingness to employ remanufacturing schemes more broadly. In response, this paper developed a comprehensive, integrated model of a remanufacturing environment based on a real-world remanufacturing system with multiple supply and demand streams. This helps industrial decision-makers to schedule their available resource effectively despite operating in a multi-product environment under supply and quality uncertainty. The developed model highlights the complicated relationship in remanufacturing systems and forms the basis for the further development of suitable control algorithms.

Keywords: Smart grid, Machine learning, Modeling
Abstract: Accurate building models are essential for developing efficient energy control systems, but creating these models is challenging. Data-driven approaches tackle the time consuming and complex calibration of models by learning building dynamics directly from sensor data. However, the lack of data from newly built buildings hinders the adoption of such methods. To address this data scarcity, transfer learning approaches can adapt a model trained on a source building to a target one with minimal data. This study evaluates different transfer learning strategies—full fine-tuning, partial fine-tuning, and ensemble methods—across three types of building thermal models. Experiments were conducted using data from the UMAR Unit of the NEST building, Dübendorf, Switzerland and the CityLearn environment using data from multiple U.S. cities. Results show that transfer learning enables models to achieve performance close to the Oracle model—trained directly on the target building’s data—while significantly reducing data requirements. Notably, partial fine-tuning maintains similar accuracy at a lower computational cost while the ensemble method, which averages fine-tuned models from different sources, can even outperform the Oracle model.

Keywords: Reduced order modeling, Mechanical systems, Computational methods
Abstract: This paper considers the problem of complexity reduction of large-scale interconnected structural-dynamics models. On the one hand, traditional CMS-based reduction methods for such problems often fail to sufficiently reduce the order of these models. On the other hand, the large-scale nature of these models obstructs direct application of more effective balanced reduction methods. To address this challenge, we propose a synergetic approach that combines several structure-preserving balanced truncation methods with various efficient Gramian approximation techniques. A comparative study of the effectiveness and computational efficiency of the resulting methods is performed by using those to reduce a structural-dynamics model from the lithography industry.

Keywords: Control applications, Process control, PID control
Abstract: The Dissolved Oxygen (DO) in the reactor plays a key role in modern wastewater treatment based on Activated Sludge Process (ASP). This paper proposes a methodology to design a DO controller based on simple black-box model identification that can be applied to any ASP plant starting from standard measurements produced applying a simple ON-OFF controller to the plant. The proposed control scheme is easily implementable on a commercial Programmable Logic Controller and SCADA system. It is tested on different technologies: a simulated Conventional Activated Sludge and a real full-size Thermophilic Aerobic Membrane Reactor (TAMR) plant. A six-month experiment in TAMR has shown an improvement in the reduction of both Chemical Oxygen Demand (COD) (80. 9% vs 66. 8%) and NO3 (94. 5% vs 85. 3%).

Keywords: Sensor networks, Biosystems, Kalman filtering
Abstract: We present a formulation for dynamic sensor scheduling for environment monitoring applications using multi-agent systems. The domain of interest is represented by a parameterized state-space model which captures spatio-temporal correlations of the environment. The parameters of the model are adapted online as new measurements become available. We introduce an improved greedy approach that simultaneously determines the optimal sensing locations and assigns agents to these locations; and this approach minimizes travel costs incurred by the mobile agents while satisfying the dynamic reachability constraints. We provide performance guarantees for the algorithms under certain conditions and derive their polynomial-time computational complexities. We demonstrate the proposed approach for bio-mass monitoring applications in large water bodies.

Keywords: LMIs, Linear systems, PID control
Abstract: This study proposes a frequency shaping method for sampled-data systems considering intersample behavior. Since there are two types of times for sampled-data systems (continuous- and discrete-time), it is necessary to unify the time to obtain frequency response. However, intersample behavior cannot be considered if the plant is discretized by focusing only on the sampling points. Therefore, this study determines the number of divisions for intersample response and obtains the frequency response at the division points to address this issue. This approach enables plotting Bode and Nyquist diagrams, including intersample behavior. Furthermore, applying the generalized Kalman-Yakubovich-Popov lemma as a frequency shaping method, the frequency domain inequality at the division points can be converted into equivalent linear matrix inequality. This method allows for the design of control systems in a straightforward yet rigorous manner.

Keywords: Nonlinear systems, Nonlinear robust control, Process control
Abstract: While a characterization of plant-model mismatch is necessary for robust control, the mismatch usually can not be described accurately due to the lack of knowledge about the plant model or the complexity of nonlinear plants. Hence, this paper considers this problem in a data-driven way, where the mismatch is captured by parametric forms of integral quadratic constraints (IQCs) and the parameters contained in the IQC equalities are learned from sampled trajectories from the plant. To this end, a one-class support vector machine (OC-SVM) formulation is proposed, and its generalization performance is analyzed based on the statistical learning theory. The proposed approach is demonstrated by a single-input-single-output time delay mismatch and a nonlinear two-phase reactor with a linear nominal model, showing accurate recovery of frequency-domain uncertainties.

Keywords: Nonlinear robust control, Variational methods, Hybrid systems
Abstract: The model-free control approach is an advanced control law that requires little information about the process to control. Since its introduction in 2008, numerous applications have been successfully considered demonstrating its robustness in tracking and disturbance rejection. In this work, a variational approach of the model-free control is proposed in order to enhance its robustness capabilities. An adaptive formulation of the controller is proposed using the calculus of variations within a symplectic framework, aiming to formulate the control law as an optimization problem for the online tuning of its key parameter. The proposed formulation establishes a coupling between the model-free control law and a variational integrator to enhance robustness against process variations and emphasize closed-loop stabilization. Illustrative examples are examined to highlight the effectiveness of the proposed approach.

Keywords: Modeling, Nonlinear systems, Automotive applications
Abstract: In this paper, data-driven algorithms based on Koopman Operator Theory are applied to identify and predict the dynamics of a vapor compression system and cabin temperature in a light-duty electric vehicle. By leveraging a high-fidelity nonlinear HVAC model, the system’s behavior is captured in a lifted higher-dimensional state space, enabling a linear representation. A comparative analysis of three Koopman-based system identification approaches—polynomial, radial basis functions (RBF), and neural network dictionary learning—is conducted, evaluating, for each of these libraries, the impact of the dimension of the lifted space on the accuracy of the model. Accurate prediction of power consumption over driving cycles is demonstrated by incorporating power as a measurable output within the Koopman framework. The performance of each method is rigorously evaluated through simulations under various driving cycles and ambient conditions, highlighting their potential for real-time prediction and control in energy-efficient vehicle climate management. This study offers a scalable, data-driven methodology that can be extended to other complex nonlinear systems.

Keywords: Neural networks, Filtering, Modeling
Abstract: The application of neural networks in modeling dynamic systems has become prominent due to their ability to estimate complex nonlinear functions. Despite their effectiveness, neural networks face challenges in long-term predictions, where the prediction error diverges over time, thus degrading their accuracy. This paper presents a neural filter to enhance the accuracy of long-term state predictions of neural network-based models of dynamic systems. Motivated by the extended Kalman filter, the neural filter combines the neural network state predictions with the measurements from the physical system to improve the estimated state’s accuracy. The neural filter’s improvements in prediction accuracy are demonstrated through applications to four nonlinear dynamical systems. Numerical experiments show that the neural filter significantly improves prediction accuracy and bounds the state estimate covariance, outperforming the neural network predictions. Furthermore, it is also shown that the accuracy of a poorly trained neural network model can be improved to the same level as that of an adequately trained neural network model, potentially decreasing the training cost and required data to train a neural network.

Keywords: Sensor fusion, Machine learning, Optimization
Abstract: In modern surveillance and defense systems, accurate tracking of aerial and ground targets remains a critical challenge due to the presence of uncertainties and dynamic target maneuvers. This study presents a robust air-to-air and air-to-ground tracking framework that integrates heterogeneous sensor measurements from radar and infrared sensors. The core methodology is based on an Extended Kalman Filter with a constant velocity motion model, further enhanced by heterogeneous track-to-track fusion. To mitigate estimation errors arising from dynamic uncertainties, a scientific machine learning-supported approach is proposed to tune the process noise covariance and sensor trackers' covariances adaptively. The proposed method refines the covariance coefficients to improve tracking accuracy under various maneuvering conditions, including climbing, descending, constant rate turns, and accelerated motions. The optimization is conducted over multiple simulated scenarios, where optimal parameters and estimated state vectors of the tracked objects serve as input features. Neural network models are then trained on these features to generalize the optimization results, enabling real-time estimation of process noise covariance coefficients in unseen scenarios. Experimental evaluations demonstrate the effectiveness of the proposed approach in adapting to dynamic target maneuvers, reducing estimation errors, and improving overall tracking performance.

Keywords: Learning, Computational methods, Power systems
Abstract: Spectral methods are a class of techniques that numerically solve a system of ordinary differential equations (ODEs) by writing its solution as a finite composition of certain basis functions and optimizing the coefficients in the sum to satisfy the differential equations as much as possible. Fourier and Chebyshev spectral methods are the well-known ones, which utilize the strong approximation capability of the Fourier and Chebyshev series to approximate the solution ODEs by closeform functions with Fourier and Chebyshev bases. By putting the vectorization of Fourier and Chebyshev basis functions in the network edge, we revisit these spectral methods with the flexibility of modern machine-learning tools and the computational strength of automatic differentiation. This leads to new physics-informed neural network (PINN) layers that can learn the solution of arbitrary ODEs as an explicit sum of known basic functions rather than as an implicit function in traditional neural network layers. Numerical results show that our approach can provide continuous and differentiable solutions for ODEs with good interpolation properties in a computationally effective manner using a smaller number of network parameters compared to traditional neural network layers in existing PINNs

Keywords: Transportation systems, Intelligent systems, Game theory
Abstract: The evolution of existing transportation systems, mainly driven by urbanization and increased availability of mobility options, such as private, profit-maximizing ride-hailing companies, calls for tools to reason about their design and regulation. To study this complex socio-technical problem, one needs to account for the strategic interactions of the stakeholders involved in the mobility ecosystem. In this paper, we present a game-theoretic framework to model multi-modal mobility systems, focusing on municipalities, service providers, and travelers. Through a user-friendly, Graphical User Interface, one can visualize system dynamics and compute equilibria for various scenarios. The framework enables stakeholders to assess the impact of local decisions (e.g., fleet size for services or taxes for private companies) on the full mobility system. Furthermore, this project aims to foster STEM interest among high school students (e.g., in the context of prior activities in Switzerland, and planned activities with the MIT museum). This initiative combines theoretical advancements, practical applications, and educational outreach to improve mobility system design.

Keywords: Navigation, Mobile Robots, Predictive control
Abstract: Autonomous navigation in off-road environments presents unique challenges, including uneven terrain, unpredictable obstacles, and localization inaccuracies. This paper introduces a robust framework for autonomous truck navigation tailored to such scenarios. The system integrates a Model Predictive Control (MPC) method that enhances path-following precision and adaptability compared to conventional controllers like the Stanley Controller. The architecture combines a local planner and an MPC-based controller within an event-driven framework to ensure dynamic path planning, obstacle avoidance, and responsiveness to environmental uncertainties. The system was evaluated in a co-simulated environment using AVL VSM™ for vehicle dynamics and CARLA for off-road terrain simulation. Results show that the MPC reduces cross-track errors and improves trajectory stability under varying conditions. Additionally, the framework demonstrated effective obstacle avoidance and adaptability to localization noise, maintaining safe and reliable operation.

Keywords: Optimization, Transportation systems, Autonomous systems
Abstract: This paper presents a method for parameterizing a linear quadratic (LQ) motion planning algorithm for automated vehicles such that it approximates the behavior of a planning algorithm that optimizes for a multi-objective optimization (MOO) objective function. This MOO objective function describes the Pareto conflicting objectives of mitigating motion sickness and reducing the travel time. Both objectives cannot be considered in the LQ motion planning algorithm due to the complex formulation of motion sickness objectives, and the limited prediction horizon that does not allow for considering the total travel time. We use an LQ planning approach because it allows for online planning. The presented method uses Bayesian optimization to tune the parameters of the LQ objective function so that the planned reference trajectory is optimal with respect to the MOO objective function. Moreover, we use a normalized weighted-sum method to generate different weights for the MOO objectives, which yields a convex Pareto front of the MOO objective function. The presented results demonstrate that the LQ motion planning algorithm is online capable and can be effectively tuned to balance the trade-off between motion sickness and travel time according to passenger susceptibility.

Keywords: Transportation systems, Intelligent systems, Stochastic/uncertain systems
Abstract: Pedestrian confidence region prediction in urban scenarios is a critical component of autonomous driving systems, especially for ensuring pedestrian safety. Recent research has utilized a physics-based pedestrian model, known as the Social Force Model (SFM), to represent pedestrian dynamics, offering deterministic predictions of pedestrian trajectories. However, these approaches often fail to account for the inherent uncertainty in pedestrian behavior and the biases between historical datasets. This paper formulates a Distributionally Robust Optimization (DRO) problem that incorporates both parameter and distributional uncertainty into the SFM to predict pedestrian confidence regions at a specified future time. To solve this, we apply a sample-based continuous approximation method that transforms the probabilistic constraints into a tractable deterministic form. The uniform convergence of the optimal solution is established. A case study of pedestrian-vehicle interaction is presented, demonstrating that the proposed approach improves the prediction accuracy of pedestrian regions. The results show that the method provides a more reliable and robust prediction of pedestrian motion, effectively accounting for uncertainties in real-world scenarios.

Keywords: Power systems, Reinforcement learning, Control applications
Abstract: This paper presents a partially model-free adaptive optimal tracking control method for power systems, specifically targeting a synchronous generator connected through a reactive transmission line. By integrating the tracking error dynamics with reference trajectory dynamics, an augmented system is created. A discounted performance function is introduced to address the nonlinear tracking problem optimally. Unlike traditional methods that compute feedforward and feedback terms separately, the proposed approach calculates both simultaneously by minimizing the discounted performance function. The discrete-time Bellman equation for tracking is derived, and a reinforcement learning (RL)-based technique is employed to solve the optimal policy online without requiring prior knowledge of system drift dynamics. Finally, the proposed method is validated through a real-time digital simulator (RTDS) with a standard power system representation.

Keywords: Power systems, Optimization, Smart grid
Abstract: The increasing penetration of photovoltaic (PV) systems in distribution grids can lead to overvoltage and transformer overloading issues. While voltage regulation has been extensively studied and some research has addressed transformer temperature control, there is limited work on simultaneously managing both challenges. This paper addresses this gap by proposing an optimization-based strategy that efficiently manages voltage regulation and transformer temperature while minimizing the curtailment of PV generation. In order to make this problem convex, a relaxation is applied to the transformer temperature dynamics constraint. We also provide analysis to determine under which conditions this relaxation remains tight. The proposed approach is validated through simulations, demonstrating its effectiveness in achieving the desired control objectives.

Keywords: Power systems, Linear systems, Complex networks
Abstract: In earlier work [1] and [2], the second author of this paper developed full-state feedback controllers for networked systems to block the observability and controllability of certain remote nodes. In this paper, we build on these control schemes to an interconnected power system with the aims of blocking (i) mode participation factors and (ii) inter-area mode observability in tie-line power flow measurements. Since participation factors depend on both controllable and observable eigenvectors, the control techniques from the cited works must be carefully tailored to this setting. Our research is motivated by cyber-security concerns in power systems, where an adversary aims to deceive the operator by tampering the system's modal content. We present extensive numerical results on a 3-machine, 9-bus system and a 16-machine, 68-bus system.

Keywords: Power Electronics, Control applications, LMIs
Abstract: In the control and operation of power inverters, proportional-resonant (PR) controllers are used to track references and reject disturbances that can be described by a sum of sinusoidal signals of known frequency. The relevance of these controllers is increasing due to the increase in distortion and volatility of three-phase signals in the grid. However, as reported in the literature, the tuning of PR controllers is far from trivial due to their large number of parameters and the presence of pure imaginary poles in the associated transfer function. To address these issues, a time-domain framework based on linear matrix inequalities (LMIs) is presented for the tuning of PR controllers with applications to voltage and current tracking. The advantage of this approach is the straightforward combination with other techniques such as H infinity control. The effectiveness of the approach is illustrated through numerical simulations, where the injection of a constant active power is achieved in the presence of distorted voltages.

Keywords: Planning, Robust control, Discrete event systems
Abstract: The Multi-Agent Path Finding (MAPF) and its extension, Multi-Agent Pickup and Delivery (MAPD), have received much attention and various algorithms have been proposed in academia. In the industrial sector, on the other hand, automatic safe control of teams of robots and AGVs on factory floors and logistic warehouses for pickup and delivery operations has also been studied intensively. However, it is still difficult for robots to fully automate all tasks in real warehouses/factories. Therefore, robots and human workers are desired to collaborate on tasks that suit each other and work at the same time in the same warehouse. In this paper, we extend the MAPD problem to a new problem with a human-in-the-loop environment where robots and human workers work at the same time in the same warehouse. We propose a robust space-time A* based multi-task MAPD algorithm that provably solves this extended problem. We also demonstrate the robustness of our proposed algorithm by comparing with the traditional (non-robust) MAPD algorithm; it is observed that workers and robots tend to gather around themselves in clusters when robust parameters are large.

Keywords: Discrete event systems, Mobile Robots, Planning
Abstract: This paper proposes and comparatively analyses different techniques of allocating tasks to mobile robots in a computationally efficient manner, with the purpose that the robotic team should satisfy an imposed Boolean formula over a set of tasks. At the same time, various tweaks in solving the path planning problem are investigated. The development starts from structural properties of the Robot Motion Petri Net model of the team that allows one to solve a sequence of Linear Programming (LP) problems instead of a Mixed Integer Linear Programming (MILP) that may become intractable for a large number of robots. We employ various strategies for iterative roundings of non-integer elements of the LP’s solution of the task allocation part of the problem, while also examining a rounding technique directly referring to the path planning step. Although the sequence of LPs returns a sub-optimal solution versus the MILP formulation, it brings a net advantage regarding the reduced runtime. The effectiveness of the approach is supported through extensive numerical simulations that compare the influence of different rounding techniques on both runtime and solution cost. The results indicate that the iterative LP-based methods offer a good trade-off between tractable computation and solution quality, especially for teams composed of a few hundred robots.

Keywords: Discrete event systems, Robotics applications, Optimization
Abstract: This work explores the application of formal techniques to the synthesis of control logic for Multi-Robot Systems (MRS). The framework proposed in the paper is applied to a scenario where multiple autonomous mobile ground robots must work cooperatively to complete a set of tasks in minimal time, subject to constraints on the ordering of the tasks and restrictions on what regions a robot can occupy. Discrete event models and Supervisory Control Theory are employed to generate models defining the range of behaviors of the MRS that satisfy the given constraints. A number of planning algorithms including Greedy, Dijkstra’s, Genetic, and Adaptive Large Neighborhood Search algorithms are then explored for selecting a ``good” sequence of actions for the robots from among the set of legal alternatives. Specifically, the planning algorithms are applied to monolithic models, as well as to hierarchical models that split the problem into a high-level task assignment problem and a low-level motion planning problem. Formal methods are employed to justify the hierarchical model and the low-level motion planning employs a receding horizon approach to planning that composes modular models on the fly. The application of the described framework is applied to simulation of the MRS scenarios in Gazebo, as well as to implementation on physical mobile robots.

Keywords: Discrete event systems, Renewable Energy, Real-time systems
Abstract: This paper presents an implementation strategy for Power Management Systems (PMSs) for microgrids (MGs) using supervisory control theory (SCT). More specifically, the present design utilizes the modular local approach for synthesizing essential functionalities of the PMS, including peak shaving and load shedding. Additionally, the design guarantees the State of Charge (SOC) of the Battery Energy System (BESS) within safe values and coordinates its operation to supply power to the grid during low-power periods. The approach is validated through a case study using a Hardware-In-the-Loop implementation with a Raspberry Pi 5 and a real-time simulator OPAL-RT.

Keywords: Predictive control, Computational methods, Process control
Abstract: Model Predictive Control is a powerful technique for dynamic optimization in various industrial applications. In many such control applications, some variables are binary in nature, i.e., either on or off. Integrating binary variables into the MPC problem, forming a mixed-binary integer MPC problem, significantly increases the complexity of the problem. A way to handle such problem complexity, the classical mixed integer branch and bound solver algorithm breaks the problem into sub-problems (nodes), forming computational branches to reach a candidate solution, and eliminating all branches that cannot obtain an optimal solution. This approach enables massive parallel processing since the nodes can be computed independently, and the GPU-enabled computing paradigm is thus a natural target. The number of nodes belonging to a specific branch grows exponentially with the number of binary variables. Since a given GPU architecture may tackle a finite number of parallel branches, the node selection procedure is crucial for the actual deployment of such methods. In our proposed method, we exploit the GPU parallelism in two ways to achieve this while explicitly taking into account the maximum feasible parallel branches for a given memory architecture. First, we explore a batch of branches in parallel to identify the branch that results in a good candidate solution. Secondly, at the same time, for a given branch, we traverse over it efficiently by skipping intermediate nodes to reach a candidate solution. Initial comparison of our proposed method with Gurobi in controlling a four-tank system shows promising results.

Keywords: Embedded systems, Reinforcement learning, Control applications
Abstract: Deep Reinforcement Learning offers a powerful approach for developing advanced control policies based solely on plant model simulations. However, deploying these policies on industrial-scale embedded microcontroller systems presents significant challenges. Imperfect plant models, parameter uncertainty, and modeling errors can compromise robust control operation, while the limited computational power of real-time microcontrollers necessitate adaptations to ensure efficient execution of learned policies.

In this work, we present a reinforcement learning-based motor control concept and investigate the impact of compute-efficient deployment techniques. We evaluate the effects of quantization on the control policy, providing key insights into how it influences long short-term memory (LSTM) cell behavior in control problem settings, and further explore the associated deployment challenges through experiments on a real-world motor control application.

Keywords: Control architectures, Real-time systems
Abstract: Digital control systems introduce unavoidable computational latencies. For some controllers this time delay inhibits practical use, even though they in theory could provide more efficient control. For example, solving an optimization problem each sampling period when using model predictive control. By sampling faster than the computation time and executing independent controllers on distributed hardware, e.g., a multi-core CPU or an edge/cloud server, output from the controllers can be interlaced. We call this oversampling-based control. This paper explores its implementation via simulations and experiments on a real Furuta pendulum using LQR and MPC control. We demonstrate both how control performance can be improved, without changing the control law, and practical issues that may occur.

Keywords: Predictive control, Optimization, Control applications
Abstract: In this work, a Model Predictive Controller (MPC) is proposed to control the plasma shape in the Tokamak à Configuration Variable (TCV). The proposed controller relies on models obtained by coupling linearized plasma response models, derived from the texttt{fge} code of the Matlab EQuilibrium toolbox (MEQ) suite, with a state-space description of the core TCV magnetic control system. It optimizes the reference signals fed to this inner control loop in order to achieve the desired plasma shape while also enforcing constraints on the plant outputs. To this end, a suitable Quadratic Programming (QP) problem is formulated and solved in real-time. The effectiveness of the proposed controller is illustrated through a combination of simulations and experimental results. To the best of our knowledge, this is the first time that a plasma shape control solution based on MPC has been experimentally tested on a real tokamak.

Keywords: Machine learning, Linear systems, Real-time systems
Abstract: Machine learning has recently been adopted to emulate sensitivity matrices for real-time magnetic control of tokamak plasmas. However, these approaches would benefit from a quantification of possible inaccuracies. We report on two aspects of real-time applicability of emulators. First, we quantify the agreement of target displacement from VCs computed via Jacobians of the shape emulators with those from finite differences Jacobians on exact Grad–Shafranov solutions. Good agreement (≈5-10%) can be achieved on a selection of geometric targets using combinations of neural network emulators with ≈ 10^5 parameters. A sample of ≈ 10^5 − 10^6 synthetic equilibria is essential to train emulators that are not over-regularised or overfitting. Smaller models trained on the shape targets may be further fine-tuned to better fit the Jacobians. Second, we address the effect of vessel currents that are not directly measured in real-time and are typically subsumed into effective “shaping currents” when designing virtual circuits. We demonstrate that shaping currents can be inferred via simple linear regression on a trailing window of active coil current measurements with residuals of only a few Ampères, enabling a choice for the most appropriate shaping currents at any point in a shot. While these results are based on historic shot data and simulations tailored to MAST-U, they indicate that emulators with few-millisecond latency can be developed for robust real-time plasma shape control in existing and upcoming tokamaks.

Keywords: Predictive control, Reinforcement learning, Control applications
Abstract: A tokamak is a toroidal device that utilizes helical magnetic fields to confine a superheated plasma. The spatial distribution of the pitch of the magnetic field, referred to as the safety factor profile, is linked to the stability and performance of the confined plasma. Thus, the capability to control the safety factor profile is essential for realizing advanced operation scenarios in tokamaks. This work proposes a Reinforcement Learning-based Model Predictive Control (RLMPC) approach for the enhanced regulation of the safety factor profile in the Experimental Advanced Superconducting Tokamak (EAST). By estimating in real time the uncertain parameters of the plasma model, the RLMPC method aims to achieve more accurate and robust tracking of the target profile. Besides learning the parameterized control-oriented response model, which serves as a linear constraint in the MPC problem, the reinforcement learning-based algorithm also learns the weight associated with the initial cost. Swift convergence of the learnable parameters within the RLMPC is facilitated through a second-order Least Squares Temporal Difference Q-learning (LSTDQ) algorithm. Simulation studies based on the Control Oriented Transport SIMulator (COTSIM) show that the proposed RLMPC performs better than conventional linear MPC schemes.