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data-driven Weighted H∞ control of Persistent Dwell Time Switched systems With Optimal Disturbance Attenuation Guaranteed

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ieee TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2025年 22卷 8162-8173页

作者： Wu, Jiacheng Lian, Bosen Su, Hongye Zhu, Yang Zhejiang Univ Inst Cyber Syst & Control State Key Lab Ind Control Technol Hangzhou 310027 Peoples R China Zhejiang Univ Ningbo Innovat Ctr Ningbo 315100 Peoples R China Auburn Univ Dept Elect & Comp Engn Auburn AL 36849 USA

Persistent dwell-time switched systems (PDTSSs) are being increasingly employed for modeling the dynamics of systems with non-uniform time-dependent switching rules. The existing attempts to design control methods for PDTSSs necessitate a prior knowledge of system dynamics. In this article, we propose a data-driven reinforcement learning (RL) method to solve the weighted 9i infinity control problem for PDTSSs with completely unknown system dynamics. The proposed method aims to calculate the weighted H-infinity control gain for PDTSSs with optimal disturbance attenuation guaranteed by solving a set of linear matrix inequalities related to system data, as opposed to solving it with the exact model information. To ensure the stability of the overall switched system, we establish the criterion for exponential mean-square stability of the closed-loop PDTSSs, as well as analyze the convergence of the proposed data-driven RL algorithm. Finally, the efficacy of the designed algorithms is illustrated via an electric circuit model.

关键词： Switched systems 9i infinity control persistent dwell time data-driven control data-driven control

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Optimal Tracking control of Second-Order Multiagent systems With Input Delay via data-driven Forward Reward Q -learning Framework

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ieee TRANSACTIONS ON systems MAN CYBERNETICS-systems 2025年第3期55卷 1858-1869页

作者： Rao, Kai Yan, Huaicheng Liu, Qiwei Dang, Qingmei Shi, Kaibo East China Univ Sci & Technol Key Lab Smart Mfg Energy Chem Proc Minist Educ Shanghai 200237 Peoples R China East China Univ Sci & Technol Key Lab Smart Mfg Energy Chem Proc Minist Educ Shanghai 200237 Peoples R China Weifang Univ Coll Machinery & Automat Weifang 261061 Peoples R China Beijing Univ Chem Technol Coll Informat Sci & Technol Beijing 100029 Peoples R China Chengdu Univ Sch Elect Informat & Elect Engn Chengdu 610106 Peoples R China

In this article, an optimal tracking control algorithm is derived for second-order discrete-time multiagent systems (MASs) with unknown system dynamics and input delay. First, the optimal tracking problem of MASs with input delay is constructed by the tracking error and a local performance index function. By designing a new variable, the original model is converted into a model without delay while guaranteeing the equivalence of performance index and control law of each agent. Subsequently, the transformed model and reinforcement learning (RL) theory are integrated to obtain a novel data-driven distributed learning framework. This framework enables online learning of the optimal control law and ensures tracking consensus of all followers' position and velocity states. Compared to the traditional actor-critic framework, an additional neural network (NN) is utilized to approximate the forward reward information (FRI) to improve the information learning capability of the MASs. Furthermore, the convergence analysis of system states and three NNs structures are conducted by Lyapunov theory. Finally, the proposed framework is verified to have better convergence and require fewer iteration steps than classical actor-critic framework by numerical simulation comparison experiments.

关键词： Delays Heuristic algorithms Performance analysis Convergence Q-learning Optimal control Artificial neural networks Approximation algorithms Delay effects System dynamics data-driven input delays optimal tracking control reinforcement learning (RL) second-order multiagent systems (MASs)

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Online Constraint Tightening in Stochastic Model Predictive control: A Regression Approach

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ieee TRANSACTIONS ON AUTOMATIC control 2025年第2期70卷 736-750页

作者： Capone, Alexandre Brudigam, Tim Hirche, Sandra Carnegie Mellon Univ Robot Inst Pittsburgh PA 15213 USA Tech Univ Munich Chair Automat Control Engn D-80333 Munich Germany Tech Univ Munich Sch Computat Informat & Technol D-80333 Munich Germany

Solving chance-constrained stochastic optimal control problems is a significant challenge in control. This is because no analytical solutions exist for up to a handful of special cases. A common and computationally efficient approach for tackling chance-constrained stochastic optimal control problems consists of a deterministic reformulation, where hard constraints with an additional constraint-tightening parameter are imposed on a nominal prediction that ignores stochastic disturbances. However, in such approaches, the choice of constraint-tightening parameter remains challenging, and guarantees can mostly be obtained assuming that the process noise distribution is known a priori. Moreover, the chance constraints are often not tightly satisfied, leading to unnecessarily high costs. This work proposes a data-driven approach for learning the constraint-tightening parameters online during control. To this end, we reformulate the choice of constraint-tightening parameter for the closed loop as a binary regression problem. We then leverage a highly expressive Gaussian process, model for binary regression to approximate the smallest constraint-tightening parameters that satisfy the chance constraints. By tuning the algorithm parameters appropriately, we show that the resulting constraint-tightening parameters satisfy the chance constraints up to an arbitrarily small margin with high probability. Our approach yields constraint-tightening parameters that tightly satisfy the chance constraints in numerical experiments, resulting in a lower average cost than three other state-of-the-art approaches.

关键词： Stochastic processes Uncertainty Symbols Optimal control Costs Computational modeling Closed loop systems Autonomous systems data-driven control Gaussian processes (GPs) machine learning online learning optimal control reinforcement learning statistical learning stochastic processes uncertain systems

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Koopman-Based Robust learning control With Extended State Observer

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ieee ROBOTICS AND AUTOMATION LETTERS 2025年第3期10卷 2303-2310页

作者： Lyu, Shangke Lang, Xin Wang, Donglin Polytech Montreal MIST Lab Montreal PQ H3T 0A3 Canada

A key challenge in data-driven robot control is enabling robots to autonomously gather the most informative data during training while maintaining robust performance when deployed in new tasks or encountering unknown external disturbances. In this paper, we propose a robust active learning (RAL) control method designed to optimize data efficiency during model learning while ensuring robust and precise control during task execution. This approach integrates Koopman-based modeling with an active learning algorithm to enhance model learning efficiency, and an extended state observer (ESO)-assisted tracking control to ensure precise robot position control in the presence of unknown disturbances. The effectiveness of the proposed method is validated through various simulations and experiments, demonstrating significant improvements in data efficiency and robustness against unknown disturbances.

关键词： Robots Active learning Aerospace electronics data models Training Robot sensing systems Nonlinear dynamical systems Adaptation models Accuracy Uncertainty Koopman operator active learning extended state observers robot control

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Event-Triggered data-driven Iterative learning control for Nonlinear MASs Under Switching Topologies

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ieee TRANSACTIONS ON FUZZY systems 2025年第4期33卷 1322-1332页

作者： Sun, Shanshan Li, Yuan-Xin Hou, Zhongsheng Liaoning Univ Technol Coll Sci Jinzhou 121001 Peoples R China Qingdao Univ Inst Complex Sci Qingdao 266071 Peoples R China

This article aims to address the problem of distributed model-free adaptive iterative learning control for nonlinear discrete-time multiagent systems under switching topologies. To save valuable bandwidth in the wireless channel without sacrificing system performance, an event-triggered iterative learning control strategy is established and employed, where information is only transmitted at triggered instants. First, by virtue of the dynamic linearization technology, the controlled system can be converted into a linear model to construct the controller structure. Second, a model-free adaptive iterative learning consensus control scheme is proposed merely employing the input and output data, in which better tracking performance can be attained by learning the previous experience. Third, a dynamic event-triggered mechanism along the iteration domain is set up to deal with the limited bandwidth issue, effectively saving communication resources. Unlike most model-free adaptive control results, the constructed distributed controller is designed based on controller-dynamic-linearization approach to deal with the controller structure design issue without designing the cost function, making it more convenient in solving tracking control issues for multiagent systems under iteration-switching communication topologies, which is more suitable for the actual environment. Using graph theory and the contraction mapping principle, the convergence of tracking control errors is theoretically analyzed. Ultimately, the effectiveness of the established control schemes is illustrated through two simulation examples.

关键词： Event detection Topology control systems Switches Adaptation models Heuristic algorithms Consensus control Bandwidth Network topology Stability analysis controller-based dynamic linearization method data-driven control event-triggered control iterative learning control multiagent systems (MASs) switching communication topologies

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Compensatory data-driven Networked Iterative learning control With Communication Constraints and DoS Attacks

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ieee TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2025年 22卷 10728-10740页

作者： Zhang, Huimin Chi, Ronghu Huang, Biao Hou, Zhongsheng Qingdao Univ Sci & Technol Coll Automat & Elect Engn Qingdao 266061 Peoples R China Univ Alberta Dept Chem & Mat Engn Edmonton AB T6G 2G6 Canada Qingdao Univ Sch Automat Qingdao 266071 Peoples R China

Considering the three critical factors of data quantization, channel fading, and denial of service (DoS) attack introduced by the networked control systems (NCSs) simultaneously, we propose a novel compensatory data-driven networked iterative learning control (COMP-DDNILC) method for nonlinear repetitive NCSs under a model-free design and analysis framework. By reformulating the iterative input-and-output (I/O) dynamics of the nonlinear NCS as an iterative linear data model (iLDM), an iterative linear predictive data model (iLPDM) is developed to predict the missing data arisen from DoS attacks. Then, a relationship is built to describe the coupling effects of the three critical factors, based on which the COMP-DDNILC is designed by involving the compensatory mechanism of DoS attacks and the fading coefficient inversion to improve the control performance. The COMP-DDNILC also involves an iterative adaption mechanism to update the iLPDM to enhance the robustness against uncertainties. The data-driven nature of COMP-DDNILC makes it applicable to practical NCSs without model information available. The simulation study verifies the results. Note to Practitioners-Networked control systems (NCSs) have becoming an important topic in practical processes owing to the shareable communication among the sensors, actuators, and controllers. However, the communication constraint and the cyber attack are inevitably induced to NCSs where the former is mainly involved with finite channel capacity and channel fading. Among different cyber attacks, denial of service (DoS) attack is most common and reachable. Further, it is worth noting that data quantization is an effective method to deal with channel capacity, but may degrade control performance by compressing I/O data. Therefore, we are motivated to propose a compensatory data-driven networked iterative learning control method by considering the three factors of data quantization, channel fading and DoS attack simultaneously to comp

关键词： Fading channels Quantization (signal) Predictive models data models Mathematical models Cyberattack Automation Vehicle dynamics Iterative learning control Costs Networked iterative learning control data quantization channel fading DoS attack data-driven design and analysis

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An Interpretable data-driven learning Approach for Nonlinear Aircraft systems With Noisy Interference

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ieee TRANSACTIONS ON AEROSPACE AND ELECTRONIC systems 2025年第1期61卷 182-194页

作者： Cao, Rui Lu, Kelin Liu, Yanbin Yangzhou Univ Coll Informat Engn & Artificial Intelligence Yangzhou 225009 Peoples R China Southeast Univ Sch Automat Nanjing 210096 Peoples R China Nanjing Univ Aeronaut & Astronaut Coll Astronaut Nanjing 210016 Peoples R China

This article discusses a learning algorithm for nonlinear aircraft systems, which targets the weaknesses of data-driven algorithms, mainly poor generalization ability and limited interpretability. It handles these constraints by integrating physical information with data-driven techniques. Referred to as the physics-informed SINDY (PI-SINDY) framework in this article, it improves the standard SINDY algorithm to tackle strongly time-varying nonlinear flight systems. This method incorporates the physical information described by the aircraft's differential kinematic equations into the SINDY algorithm and can also deal with the effects of measurement noise, making it more robust and practical. The proposed method displays higher robustness and generalization ability in comparison with the original SINDY algorithm and the WSINDy method, as confirmed by simulation results. Finally, we use the nonlinear system model learned with the suggested method for tracking control to supplement its efficiency.

关键词： Noise Noise measurement Mathematical models Aircraft Aerospace electronics Heuristic algorithms Aerospace and electronic systems data-driven learning algorithm long-term evolution analysis nonlinear aircraft systems physical information sparse identification of nonlinear dynamics (SINDy)

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Adaptive Regulated Sparsity Promoting Approach for data-driven Modeling and control of Grid-Connected Solar Photovoltaic Generation

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ieee TRANSACTIONS ON SUSTAINABLE ENERGY 2025年第1期16卷 560-572页

作者： Zhang, Zhongtian Khazaei, Javad Blum, Rick S. Lehigh Univ Dept Elect & Comp Engn Bethlehem PA 18015 USA

This paper introduces a new statistical learning technique based on sparsity promotion for data-driven modeling and control of solar photovoltaic (PV) systems. Compared with conventional sparse regression techniques that might introduce computational complexities when the number of candidate functions increases, an innovative algorithm, named adaptive regulated sparse regression (ARSR) is proposed. The ARSR adaptively regulates the hyperparameter weights of candidate functions to best represent the dynamics of PV systems. This method allows for the application of different sparsity-promoting hyperparameters for each state variable, whereas the conventional approach uses the same hyperparameter for all state variables, which may result in not excluding all the unrelated terms from the dynamics. Consequently, the proposed method can identify more complex dynamics with greater accuracy. Utilizing this algorithm, open-loop and closed-loop models of single-stage and two-stage PV systems are obtained from measurements and are utilized for control design purposes. Moreover, it is demonstrated that the proposed data-driven approach can be successfully employed for fault analysis studies, which distinguishes its capabilities from other data-driven techniques. Finally, the proposed approach is validated through real-time simulations.

关键词： Power system dynamics Adaptation models Nonlinear dynamical systems Voltage control Mathematical models Analytical models Heuristic algorithms data-driven modeling Photovoltaic systems Adaptive systems Adaptive regulated sparse regression (ARSR) closed-loop data-driven modeling photovoltaic (PV) systems single-stage PV two-stage PV

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Group Formation Tracking of Heterogeneous Multiagent systems Using Reinforcement learning

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ieee TRANSACTIONS ON control OF NETWORK systems 2025年第1期12卷 497-509页

作者： Wang, Yuhan Wang, Zhuping Zhang, Hao Yan, Huaicheng Tongji Univ Shanghai Res Inst Intelligent Autonomous Syst Shanghai 201210 Peoples R China Natl Key Lab Autonomous Intelligent Unmanned Syst Shanghai 201210 Peoples R China Frontiers Sci Ctr Intelligent Autonomous Syst Mini Shanghai 201210 Peoples R China Tongji Univ Coll Elect & Informat Engn Dept Control Sci & Engn Shanghai 201804 Peoples R China Key Lab Adv Control & Optimizat Chem Proc Minist E Shanghai 200237 Peoples R China East China Univ Sci & Technol Sch Informat Sci & Engn Shanghai 200237 Peoples R China

This article introduces a novel distributed control protocol to address the problem of output group formation tracking for heterogeneous multiagent systems. In contrast to the existing group formation control protocols that rely on the system matrices, our approach leverages input-state data to design the optimal control gains in the framework of off-policy reinforcement learning. Specifically, an event-triggered distributed consensus estimator is proposed to estimate the leaders' system matrices and the convex hulls spanned by the leaders while ensuring the exclusion of Zeno behavior. Based on the proposed estimator, we establish an approximate optimal distributed control protocol for each follower to achieve output group formation tracking, which can be solved by the designed data-driven policy iteration algorithm. Finally, a numerical example is provided to show the efficacy of the proposed control approach.

关键词： Protocols Formation control Vectors control systems Network systems Optimal control Symmetric matrices Decentralized control System dynamics Event detection data-driven policy iteration heterogeneous multiagent systems (MASs) optimal control output group formation

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data-driven Inverse Reinforcement learning for Heterogeneous Optimal Robust Formation control

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ieee TRANSACTIONS ON CYBERNETICS 2025年第5期55卷 2024-2037页

作者： Golmisheh, Fatemeh Mahdavi Shamaghdari, Saeed Iran Univ Sci & Technol Elect Engn Dept Tehran *** Iran

This article presents novel data-driven inverse reinforcement learning (IRL) algorithms to optimally address heterogeneous formation control problems in the presence of disturbances. We propose expert-estimator-learner multiagent systems (MASs) as independent systems with similar interaction graphs. First, a model-based IRL algorithm is introduced for the estimator MAS to determine its optimal control and reward functions. Using the estimator IRL algorithm results, a robust algorithm for model-free IRL is presented to reconstruct the learner MAS's optimal control and reward functions without knowing the learners' dynamics. Therefore, estimator MAS aims to estimate experts' desired formation and learner MAS wants to track the estimators' trajectories optimally. As a final step, data-driven implementations of these proposed IRL algorithms are presented. Consequently, this research contributes to identifying unknown reward functions and optimal controls by conducting demonstrations. Our analysis shows that the stability and convergence of MASs are thoroughly ensured. The effectiveness of the given algorithms is demonstrated via simulation results.

关键词： Heuristic algorithms Optimal control Formation control Trajectory Stability analysis Vectors Games Asymptotic stability Performance analysis Multi-agent systems inverse optimal control (IOC) inverse reinforcement learning (IRL) optimal control zero-sum games

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