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distributed learning control for High-Speed Trains Subject to Operation Safety Constraints

IEEE TRANSACTIONS ON CYBERNETICS 2024年第3期54卷 1794-1805页

作者： Gao, Shuai Song, Qijiang Shen, Dong Renmin Univ China Sch Math Beijing 100872 Peoples R China

Trajectory tracking problems related to high-speed trains (HSTs) are fundamental issues that affect the operation safety and ride comfort. This study proposes a distributed learning control scheme based on a multiagent system framework for trajectory tracking of HSTs subject to operation safety constraints. Two different connection modes are considered between the carriages: 1) a soft connection achieved via information exchange and 2) a hard connection enforced via couplers. Both relative displacement and speed constraints are carefully analyzed in the tracking control process. By constructing an appropriate barrier composite energy function, the convergence of the tracking error and satisfaction of the operation safety constraints are rigorously proven for the proposed scheme. The theoretical results are verified using numerical simulations.

关键词： Safety Trajectory tracking Resistance Couplers Force Mathematical models Task analysis Barrier composite energy function (BCEF) distributed learning control high-speed train (HST) operation safety tracking control

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Synchronizability-Based distributed learning control for Multi-Agent Systems

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS 2024年第4期71卷 2109-2113页

作者： Wu, Yuxin Meng, Deyuan Beihang Univ BUAA Res Div 7 Beijing 100191 Peoples R China Beihang Univ BUAA Sch Automat Sci & Elect Engn Beijing 100191 Peoples R China

In this brief, the distributed learning control problem is addressed for multi-agent systems with irregular dynamics by focusing on the transient synchronization objective. First, the synchronizability property is defined to reveal whether the multi-agent system has the ability to reach the transient synchronization objective, for which a synchronizability criterion is developed in a distributed manner. Then, given the synchronizability property, a distributed learning control protocol is further proposed through the full usage of the experience information from neighbor agents. Under the quasi-strong connectivity, the necessary and sufficient condition is exploited to accomplish the transient synchronization objective for the multi-agent system despite whether it is regular. In particular, the specific selection condition on the gain matrix is presented to provide a fully distributed design manner for the proposed distributed learning control protocol.

关键词： Multi-agent systems Distance learning Computer aided instruction Synchronization Transient analysis Protocols Trajectory distributed learning control multi-agent system synchronizability property transient synchronization

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distributed learning control for heterogeneous linear multi-agent networks

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AUTOMATICA 2024年 169卷

作者： Meng, Deyuan Zhang, Jingyao Beihang Univ BUAA Res Div 7 Beijing 100191 Peoples R China Beihang Univ BUAA Sch Automat Sci & Elect Engn Beijing 100191 Peoples R China

This paper deals with cooperative output tracking problems for heterogeneous networks of linear agents. To refine high-precision tracking performances of agents, a graph-based distributed learning control (DLC) law is proposed, for which a new bounded-initialization, bounded-updating (BIBU) stability property is explored under any bounded initial conditions. Moreover, a class of heterogeneousto-homogeneous transformation methods is introduced, together with presenting feasible gain design conditions, for DLC. It is shown that with the designed DLC law, not only can the effect of the agents' heterogeneous dynamics in performing DLC be well overcome, but also the BIBU stability and the robust cooperative output tracking of agents can be simultaneously accomplished. A simulation test is also implemented to verify the validity of our developed DLC results for heterogeneous vehicle networks. (c) 2024 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

关键词： Cooperative tracking distributed learning control Heterogeneous network Linear agent Stability

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Trackability-Based distributed learning control for Multi-Agent Systems Under Switching Topologies 62

Trackability-Based Distributed Learning Control for Multi-Ag...

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62nd IEEE Conference on Decision and control (CDC)

作者： Wu, Yuxin Meng, Deyuan Wang, Jing Beihang Univ BUAA Res Div 7 Beijing 100191 Peoples R China Beihang Univ BUAA Sch Automat Sci & Elect Engn Beijing 100191 Peoples R China North China Univ Technol Sch Elect & Control Engn Beijing 100144 Peoples R China

ISBN: (纸本)9798350301243

This paper aims to address the distributed learning control problem for irregular multi-agent systems subject to switching topologies. The cooperative trackability property for the desired reference is discussed, which ensures the existence of the desired inputs for realizing the cooperative perfect tracking objective. Then, a trackability-based distributed learning control algorithm is presented with the integration of the complete experience information from the previous iteration. It is shown that for the cooperatively trackable desired reference, all agents learn to achieve the cooperative perfect tracking objective in the presence of the developed distributed learning control algorithm despite their irregular dynamics, provided that their associated directed graphs jointly have a spanning tree. The simulation is implemented to illustrate the validity of the trackability-based distributed learning control algorithm.

关键词： Cooperative perfect tracking cooperative trackability property distributed learning control multi-agent system switching topology

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Cooperative Fault-Tolerant Output Regulation for Multiagent Systems by distributed learning control Approach

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND learning SYSTEMS 2020年第11期31卷 4831-4841页

作者： Deng, Chao Che, Wei-Wei Shi, Peng Qingdao Univ Inst Complex Sci Qingdao 266071 Peoples R China Univ Adelaide Sch Elect & Elect Engn Adelaide SA 5005 Australia

In this article, a new distributed learning control approach is proposed to address the cooperative fault-tolerant output regulation problem for linear multiagent systems with actuator faults. First, a distributed estimation algorithm with an online learning mechanism is presented to identify the system matrix of the exosystem and to estimate the state of the exosystem. In particular, an auxiliary variable is introduced in the distributed estimation algorithm to construct a data matrix, which is used to learn the system matrix of the exosystem for each subsystem. In addition, by resetting the state of the estimator and by using the identified matrix to update the estimator, all subsystems can reconstruct the state of the exosystem at an initial period of time, which is used for the neighbor subsystem to learn the system matrix of the exosystem. Based on the designed estimator, a novel distributed fault-tolerant controller is developed. Compared with the existing cooperative output regulation results, the system matrix of the exosystem considered in this article is unknown for all subsystems. Finally, a simulation example is provided to show the effectiveness of the obtained new design techniques.

关键词： Fault tolerance Fault tolerant systems Actuators Regulation Observers Adaptive systems Adaptive control cooperative fault-tolerant output regulation distributed learning control multiagent systems (MASs)

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learning-Based distributed Resilient Fault-Tolerant control Method for Heterogeneous MASs Under Unknown Leader Dynamic

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND learning SYSTEMS 2022年第10期33卷 5504-5513页

作者： Deng, Chao Jin, Xiao-Zheng Che, Wei-Wei Wang, Hai Qilu Univ Technol Sch Comp Sci & Technol Shandong Acad Sci Jinan 250353 Peoples R China Shandong Lab Comp Networks Jinan 250014 Peoples R China Qingdao Univ Inst Complex Sci Qingdao 266071 Peoples R China Murdoch Univ Discipline Engn & Energy Murdoch WA 6150 Australia

In this article, we consider the distributed fault-tolerant resilient consensus problem for heterogeneous multiagent systems (MASs) under both physical failures and network denial-of-service (DoS) attacks. Different from the existing consensus results, the dynamic model of the leader is unknown for all followers in this article. To learn this unknown dynamic model under the influence of DoS attacks, a distributed resilient learning algorithm is proposed by using the idea of data-driven. Based on the learned dynamic model of the leader, a distributed resilient estimator is designed for each agent to estimate the states of the leader. Then, a new adaptive fault-tolerant resilient controller is designed to resist the effect of physical failures and network DoS attacks. Moreover, it is shown that the consensus can be achieved with the proposed learning-based fault-tolerant resilient control method. Finally, a simulation example is provided to show the effectiveness of the proposed method.

关键词： Fault tolerance Fault tolerant systems Denial-of-service attack Circuit faults Resists Observers Actuators Adaptive control distributed learning control fault-tolerant control multiagent systems (MASs)

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Cooperative Fault-Tolerant control for a Class of Nonlinear MASs by Resilient learning Approach

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND learning SYSTEMS 2024年第1期35卷 670-679页

作者： Deng, Chao Yue, Dong Che, Wei-Wei Xie, Xiangpeng Nanjing Univ Posts & Telecommun Inst Adv Technol Nanjing 210003 Peoples R China Qingdao Univ Inst Complex Sci Qingdao 266071 Peoples R China

In this article, a learning-based resilient fault-tolerant control method is proposed for a class of uncertain nonlinear multiagent systems (MASs) to enhance the security and reliability against denial-of-service (DoS) attacks and actuator faults. With the framework of cooperative output regulation, the developed algorithm consists of designing a distributed resilient observer and a decentralized fault-tolerant controller. Specifically, by using the data-driven method, an online resilient learning algorithm is first presented to learn the unknown exosystem matrix in the presence of DoS attacks. Then, a distributed resilient observer is proposed working against DoS attacks. In addition, based on the developed observer, a decentralized adaptive fault-tolerant controller is designed to compensate for actuator faults. Moreover, the convergence of error systems is shown by using the Lyapunov stability theory. The effectiveness of our result is examined by a simulation example.

关键词： Fault tolerant systems Fault tolerance Actuators Denial-of-service attack Observers Security Regulation Actuator faults adaptive control distributed learning control fault-tolerant control

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Optimal distributed learning for disturbance rejection in networked non-linear games under unknown dynamics

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IET control THEORY AND APPLICATIONS 2019年第17期13卷 2838-2848页

作者： Tatari, Farzaneh Vamvoudakis, Kyriakos G. Mazouchi, Majid Semnan Univ Elect Engn Dept Semnan Iran Georgia Inst Technol Daniel Guggenheim Sch Aerosp Engn Atlanta GA 30332 USA Ferdowsi Univ Mashhad Elect Engn Dept Mashhad Razavi Khorasan Iran

In this study, an online distributed optimal adaptive algorithm is introduced for continuous-time non-linear differential graphical games under unknown systems subject to external disturbances. The proposed algorithm learns online the approximate solution to the coupled Hamilton-Jacobi-Isaacs equations. Each of the players in the game uses an actor-critic network structure and an intelligent identifier to find the unknown parameters of the systems. The authors use recorded past observations concurrently with current data to speed up convergence by exploring the state space. The closed-loop stability and convergence of the policies to Nash equilibrium are ensured by using Lyapunov stability theory. Finally, a simulation example shows the efficiency of the proposed algorithm.

关键词： continuous time systems convergence nonlinear control systems differential games optimal control closed loop systems approximation theory Lyapunov methods stability distributed control distributed learning control Lyapunov stability theory Nash equilibrium continuous-time nonlinear differential games networked nonlinear games optimal distributed learning closed-loop stability intelligent identifier actor-critic network structure coupled Hamilton-Jacobi-Isaacs equations optimal adaptive algorithm unknown dynamics disturbance rejection

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