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Data-driven output regulation control for constrained linear systems

science China(Information sciences) 2025年第3期68卷 338-353页

作者： Chaoyu XIA Yi DONG Chaoli WANG Shengyuan XU Shanghai Research Institute for Intelligent Autonomous Systems Tongji University College of Electronic and Information Engineering Shanghai Research Institute for Intelligent Autonomous SystemsTongji University Department of Control Science and Engineering School of Optical-Electrical and Computer EngineeringUniversity of Shanghai for Science and Technology Department of Automation Nanjing University of Science and Technology

This study introduces a data-driven approach for state and output feedback control addressing the constrained output regulation problem in unknown linear discrete-time systems. Our method ensures effective tracking performance while satisfying the state and input constraints, even when system matrices are not available. We first establish a sufficient condition necessary for the existence of a solution pair to the regulator equation and propose a data-based approach to obtain the feedforward and feedback control gains for state feedback control using linear programming. Furthermore, we design a refined Luenberger observer to accurately estimate the system state, while keeping the estimation error within a predefined set. By combining output regulation theory, we develop an output feedback control strategy. The stability of the closed-loop system is rigorously proved to be asymptotically stable by further leveraging the concept of λ-contractive sets.

关键词： output regulation constrained system data-driven.

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Time-Varying Formation Tracking control of Heterogeneous Multi-Agent Systems With Intermittent Communications and Directed Switching Networks

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IEEE/CAA Journal of Automatica Sinica 2025年第1期12卷 294-296页

作者： Yuhan Wang Zhuping Wang Hao Zhang Huaicheng Yan the Shanghai Research Institute for Intelligent Autonomous Systems Tongji University the National Key Laboratory of Autonomous Intelligent Unmanned Systems the Frontiers Science Center for Intelligent Autonomous System of Ministry of Education the College of Electronic and Information Engineering the Department of Control Science and Engineering Tongji University the Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education the School of Information Science and Engineering East China University of Science and Technology

Dear Editor,This letter is concerned with the problem of time-varying formation tracking for heterogeneous multi-agent systems(MASs) under directed switching networks. For this purpose, our first step is to present so... 详细信息

关键词： Switches Topology Observers Formation control Event detection Switched systems Multi-agent systems control theory Autonomous systems Stability

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Dynamic Regret for Distributed Online Composite Optimization

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IEEE Transactions on Automatic control 2025年第5期70卷 3056-3071页

作者： Hou, Ruijie Yu, Yang Li, Xiuxian Tongji University Department of Control Science and Engineering College of Electronics and Information Engineering Shanghai201800 China Tongji University Shanghai Research Institute for Intelligent Autonomous Systems National Key Laboratory of Autonomous Intelligent Unmanned Systems Frontiers Science Center for Intelligent Autonomous Systems Ministry of Education Shanghai Institute of Intelligent Science and Technology Shanghai201210 China

This article focuses on online composite optimization over multiagent networks. In the distributed setting, each agent has its own local loss function, which consists of a convex, strongly convex or strongly convex and smooth function, and a time-varying nonsmooth regularizer. Two distributed online algorithms are proposed and corresponding dynamic regrets are analyzed. Two proposed algorithms are based on signs of relative states. The first algorithm obtains (Formula presented) dynamic regret bound when each local loss is a general convex composite function, where CT is the path variation. If CT can be estimated in advance for convex or strongly convex local loss with a time-varying nonsmooth regularizer, then dynamic regret bounds are, respectively, in the order of (Formula presented) and o (log T(1 + CT)). The second algorithm is based on the first one, especially for handling the local loss composed of a strongly convex and smooth function with a nonsmooth regularizer, and then obtains o (1 + CT) dynamic regret bound. In the end, numerical results are given to support the theoretical findings. © 1963-2012 IEEE.

关键词： Optimization algorithms

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An interaction-fair semi-decentralized trajectory planner for connected and autonomous vehicles

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Autonomous intelligent Systems 2025年第1期5卷 1-20页

作者： Liu, Zhengqin Lei, Jinlong Yi, Peng Hong, Yiguang Department of Control Science and Engineering Tongji University Shanghai201804 China The Shanghai Research Institute for Intelligent Autonomous Systems Shanghai201804 China Shanghai Institute of Intelligent Science and Technology Tongji University Shanghai201804 China

Lately, there has been a lot of interest in game-theoretic approaches to the trajectory planning of autonomous vehicles (AVs). But most methods solve the game independently for each AV while lacking coordination mechanisms, and hence result in redundant computation and fail to converge to the same equilibrium, which presents challenges in computational efficiency and safety. Moreover, most studies rely on the strong assumption of knowing the intentions of all other AVs. This paper designs a novel autonomous vehicle trajectory planning approach to resolve the computational efficiency and safety problems in uncoordinated trajectory planning by exploiting vehicle-to-everything (V2X) technology. Firstly, the trajectory planning for connected and autonomous vehicles (CAVs) is formulated as a game with coupled safety constraints. We then define the interaction fairness of the planned trajectories and prove that interaction-fair trajectories correspond to the variational equilibrium (VE) of this game. Subsequently, we propose a semi-decentralized planner for the vehicles to seek VE-based fair trajectories, in which each CAV optimizes its individual trajectory based on neighboring CAVs’ information shared through V2X, and the roadside unit takes the role of updating multipliers for collision avoidance constraints. The approach can significantly improve computational efficiency through parallel computing among CAVs, and enhance the safety of planned trajectories by ensuring equilibrium concordance among CAVs. Finally, we conduct Monte Carlo experiments in multiple situations at an intersection, where the empirical results show the advantages of SVEP, including the fast computation speed, a small communication payload, high scalability, equilibrium concordance, and safety, making it a promising solution for trajectory planning in connected traffic scenarios. To the best of our knowledge, this is the first study to achieve semi-distributed solving of a game with coupled constr

关键词： Autonomous vehicles

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Development and Application of Coverage control Algorithms: A Concise Review

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IEEE Transactions on Automation science and engineering 2025年 22卷 14906-14927页

作者： Cheng, Bin He, Mingyuan Zhu, Zhongpan He, Bin Chen, Jie Tongji University Department of Control Science and Engineering Shanghai201804 China National Key Laboratory of Autonomous Intelligent Unmanned Systems Shanghai201203 China

Coverage control is a foundational domain within multi-agent systems, which has recently undergone substantial advancements. Model-based and optimization-based control methods have achieved new breakthroughs and applications, while data-driven and learning-based algorithms in coverage control have also yielded significant results. This review examines the evolution and application of coverage control algorithms in multi-agent systems. Moreover, this review then focuses on how contemporary algorithms build on traditional control strategies and integrate cutting-edge data-driven and adaptive learning techniques. Finally, it explores potential future directions, emphasizing the importance of interdisciplinary approaches in overcoming existing challenges and seizing new opportunities in coverage control deployment. This review aims to be a valuable resource for researchers and practitioners, guiding continued exploration in this field. Note to Practitioners - This work was motivated by the need to provide an overview of the development and research of existing multi-agent coverage control algorithms. Different application scenarios and constraints require different methods of multi-agent coverage control, each with varying efficiencies. For researchers and practitioners, deciding which coverage control method to use in specific scenarios, while considering practical constraints such as energy limitations, can be challenging. Therefore, this work offers an overview of existing multi-agent coverage control algorithms and suggests potential application scenarios. This review serves as a comprehensive reference for researchers and practitioners in the field, helping them identify suitable solutions to ensure efficient coverage. © 2004-2012 IEEE.

关键词： Learning algorithms

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Optimal Adaptive Parameter Estimation With Online Varying Learning Gain

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IEEE Transactions on Automatic control 2025年第5期70卷 3518-3525页

作者： Chen, Siyu Na, Jing Huang, Yingbo Xing, Yashan Zhao, Jing Wong, Pak Kin Kunming University of Science and Technology Faculty of Mechanical and Electrical Engineering Yunnan Key Laboratory of Intelligent Control and Application Kunming650500 China University of Macau Department of Electromechanical Engineering 999078 China

It has been well known that the learning gain plays a crucial role in the adaptive parameter estimation (APE) for guaranteeing fast convergence and robustness. However, the tuning of learning gains in the existing methods is generally empirical and time-consuming. To address this issue, this article presents a novel APE approach to explore the optimality principle in the design of adaptive laws to obtain an online updated optimal learning gain, which is derived to minimize a cost function of estimation error with two weighting matrices. For this purpose, the estimation error is first reconstructed by using filter operations on the system and introducing several auxiliary variables. Then, two constructive APE algorithms driven by the extracted estimation error are developed. Finally, inspired by the duality of control and estimation, the estimation error dynamics are reformulated as a closed-loop system with an analogous control action to minimize the cost function. By using the Hamiltonian function, a differential Riccati equation is derived to find an optimal solution of learning gain to construct an optimal adaptive law. The robustness of the proposed adaptive laws subjected to disturbances is also analyzed. Comparative numerical simulations are given to illustrate the superiority of the proposed method. © 1963-2012 IEEE.

关键词： Cost functions

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Aggressive and robust low-level control and trajectory tracking for quadrotors with deep reinforcement learning

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Neural Computing and Applications 2025年第3期37卷 1223-1240页

作者： Chen, Shiyu Li, Yanjie Lou, Yunjiang Lin, Ke Department of Control Science and Engineering Harbin Institute of Technology Shenzhen Shenzhen518055 China Guangdong Key Laboratory of Intelligent Morphing Mechanisms and Adaptive Robotics Shenzhen518055 China

Executing accurate trajectory tracking tasks using a high-performance low-level controller is crucial for quadrotors to be applied in various scenarios, especially those involving uncertain disturbances. However, due to the uncertainties in disturbed environments, developing effective low-level controllers with traditional model-based control schemes is challenging. This paper presents an aggressive and robust reinforcement learning (RL)-based low-level control policy for quadrotors. The policy maps the observed quadrotor state directly to motor thrust commands, without requiring the quadrotor dynamics. Additionally, a trajectory generation pipeline is developed to improve the accuracy of trajectory tracking tasks based on differential flatness. With the learned low-level control policy, extensive simulations and real-world experiments are implemented to validate the performance of the policy. The results indicate that our RL-based low-level control policy outperforms traditional proportional–integral–derivative (PID) control methods and related learning-based policies in terms of accuracy and robustness, particularly in environments with uncertain disturbances. Furthermore, the proposed RL-based control policy exhibits an aggressive response in trajectory tracking, even when the speed of the desired trajectory is increased to 6 m/s. Moreover, the learned policy demonstrates strong vibration suppression capabilities and enables the quadrotor to recover to a hovering state from random initial conditions with shorter response time. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Deep reinforcement learning

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Collaborative Routing and Charging/Discharging Scheduling of Electric Autonomous Vehicles in Coupled Power-Traffic Networks: A Multi-Objective Approach

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IEEE Internet of Things Journal 2025年第11期12卷 17753-17764页

作者： Chu, Kai-Fung Chen, Tianlun Xie, Yue Lam, Albert Y.S. Song, Yue Iida, Fumiya University of Cambridge Department of Engineering CambridgeCB2 1PZ United Kingdom The University of Hong Kong Department of Electrical and Electronic Engineering Hong Kong The University of Hong Kong Department of Electrical and Electronic Engineering Department of Geography Hong Kong Fano Labs Hong Kong Tongji University Department of Control Science and Engineering Shanghai201804 China National Key Laboratory of Autonomous Intelligent Unmanned Systems Shanghai201210 China Ministry of Education Frontiers Science Center for Intelligent Autonomous Systems Shanghai200120 China

Autonomous vehicles (AVs) are vehicles that traverse on the road without active human intervention. With a coordinator, AVs can be connected to provide high-efficiency transport services, such as AV-based public transport networks. The controller can manage the network by coordinating the transport request assignment, traveling, and charging/discharging schedule. On the other hand, AVs are likely to be electric and benefit the smart grid via vehicle-to-grid technology. A well-designed mobility network connecting electric AVs (EAVs) and smart grid can substantially reduce unnecessary travel and energy costs. In this paper, we aim to maximize utilities in the AV-based public transport network and the power distribution network for the vehicle network containing EAVs, charging stations, and distributed power generations. We formulate the assignment and scheduling problem as a multi-objective mixed-integer program. To solve the optimization problem, we develop a hybrid heuristic approach based on Non-Dominated Sorting Genetic Algorithm II and branch-and-bound algorithms. Experiments are conducted on a modified 15-bus distribution system and a simulated traffic network. The results show that the proposed strategy effectively minimizes the total travel and energy purchase cost by 21%. This study provides valuable insights on vehicle coordination for multiple tasks, offering visionary guidance for stakeholders engaged in multifaceted transportation endeavors. © 2014 IEEE.

关键词： Vehicle-to-grid

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Coprime Factorization-Based Encryption and Attack Detection for Nonlinear Cyber-Physical Systems Using Deep Learning Approach

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IEEE Transactions on Automation science and engineering 2025年 22卷 14020-14029页

作者： Wu, Shimeng Luo, Hao Zhang, Jiusi Qiao, Xinyu Tian, Jilun Jiang, Yuchen Harbin Institute of Technology Department of Control and Simulation Center Harbin150001 China National Key Laboratory ofComplex System Control and Intelligent Agent Cooperation Harbin150001 China University of Electronic Science and Technology of China School of Automation Engineering Chengdu611731 China

This paper presents a data-driven framework for integrating encryption transmission and attack detection in cyber-physical systems (CPS) with nonlinear physical plants. The main focus of this research is to use deep neural networks to realize the coprime factorization (CF) of nonlinear systems. The definition of the CF guides the network training and designing process, and the model’s topology is designed in the state-space form, which improves the interpretability of the data-driven CF. Based on the CF-aided neural networks, an encrypted transmission module is designed that projects information related to system dynamics into a perpendicular data space, which complements existing encryption methods from a control theory perspective. Subsequently, an anomaly detector are designed using the same CF pairs. This detector not only provides high-accuracy detection of attacks but also distinguishes between attacks and faults, thereby reducing the false positive rate and enhancing the reliability of the attack detection. The proposed method has been validated in a real CPS using a mecanum-wheeled vehicle as the physical plant, demonstrating its effectiveness and applicability. © 2004-2012 IEEE.

关键词： Factorization

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Robust Adaptive Sliding Mode Security control of Markov Jump Cyber-Physical Systems With Stochastic Injection Attacks Through Event-Triggered-Based Observer Approach

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IEEE Transactions on Systems, Man, and Cybernetics: Systems 2025年第5期55卷 3679-3692页

作者： Jiang, Baoping Niu, Fuzhou Wu, Zhengtian Qiu, Jianbin Suzhou University of Science and Technology School of Electronic and Information Engineering Suzhou215009 China Politecnico di Milano Department of Mechanical Engineering Milan20156 Italy Research Institute of Intelligent Control and Systems Harbin Institute of Technology Harbin150001 China

This article addresses the challenge of state observer design for sliding mode security control in Markov jump cyber-physical systems subjected to stochastic injection attacks. To enhance network efficiency, a dynamic event-triggered algorithm is introduced in the communication channel. First, the design begins with a Luenberger state observer featuring an adaptive compensator. This configuration aims to effectively counteract malicious attacks. Second, an integral sliding hyperplane is formulated within the estimation space, which serves as the foundation for deriving the sliding mode dynamics, ensuring robustness against disturbances. Recognizing the diversity of transition rates (TRs), an elastic sliding mode controller is designed to accommodate three distinct types of TRs, which is also strategically designed to guarantee reachability and maintain sliding motion. Third, stochastic stability with an H∞ attenuation level is conducted separately for each type of TR. Correspondingly, the development of an algorithm for determining threshold parameters in triggered conditions is presented. Simultaneously, a proof of the nonexistence of Zeno behavior is provided, ensuring the stability and efficiency of the proposed system. Finally, a simulation study using a practical model is included to empirically demonstrate the validity of the proposed method in a real-world context. © 2013 IEEE.

关键词： Stochastic systems

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