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Value Iteration-Based Distributed Adaptive Dynamic Programming for Multi-Player Differential Game With Incomplete information

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IEEE/CAA Journal of Automatica Sinica 2025年第2期12卷 436-447页

作者： Yun Zhang Yuqi Wang Yunze Cai the Department of Automation Shanghai Jiao Tong University the key laboratory of system control and information processing Ministry of Education of China the National key laboratory of Air-based information Perception and Fusion

In this paper, a distributed adaptive dynamic programming(ADP) framework based on value iteration is proposed for multi-player differential games. In the game setting,players have no access to the information of others' system parameters or control laws. Each player adopts an on-policy value iteration algorithm as the basic learning framework. To deal with the incomplete information structure, players collect a period of system trajectory data to compensate for the lack of information. The policy updating step is implemented by a nonlinear optimization problem aiming to search for the proximal admissible policy. Theoretical analysis shows that by adopting proximal policy searching rules, the approximated policies can converge to a neighborhood of equilibrium policies. The efficacy of our method is illustrated by three examples, which also demonstrate that the proposed method can accelerate the learning process compared with the centralized learning framework.

关键词： Distributed adaptive dynamic programming incomplete information multi-player differential game (MPDG) value iteration

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

Turbulent drag reduction by sector-shaped counter-flow dielectric barrier discharge plasma actuator

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Chinese Physics B 2025年第2期34卷 362-370页

作者： Borui Zheng Shaojie Qi Minghao Yu Jianbo Zhang Linwu Wang Dongliang Bian School of Automation and information Engineering Xi’an University of TechnologyXi’an 710048China School of Mechanical and Precision Instrument Engineering Xi’an University of TechnologyXi’an 710048China Science and Technology on Plasma Dynamics laboratory Air Force Engineering UniversityXi’an 710038China Shaanxi key laboratory of complex system control and intelligent information processing Xi’an 710048China Shaanxi University key laboratory of Photonic Power Devices and Discharge Regulation Xi’an 710048China

The primary objective in aircraft transportation is to minimize turbulent drag, thereby conserving energy and reducing emissions. We propose a sector-shaped counter-flow dielectric barrier discharge plasma actuator, which leverages jet synthesis for drag reduction. A drag control experiment was conducted in a low-speed wind tunnel with a controlled flow velocity of 9.6 m/s(Re = 1.445 × 10^(4)). This study investigated the effects of varying pulse frequencies and actuation voltages on the turbulent boundary layer. Using a hot-wire measurement system, we analyzed the pulsating and time-averaged velocity distributions within the boundary layer to evaluate the streamwise turbulent drag reduction. The results show that the local TDR decreases as the pulse frequency increases, reaching a maximum reduction of approximately 20.97% at a pulse frequency of 50 Hz. In addition, as the actuation voltage increases, the friction coefficient decreases, increasing the drag reduction rate. The maximum drag reduction of approximately 33.34% is achieved at an actuation voltage of 10 kV.

关键词： plasma flow control turbulent boundary layer turbulent drag reduction

Strong Structural controllability of Multi-Agent systems with Uncertain Connection Topologies

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Journal of systems Science & complexity 2025年

作者： CHEN Qitian LI Shaoyuan Department of Automation Shanghai Jiao Tong University key laboratory of system control and information processing Ministry of Education of China

This paper investigates the strong structural controllability(SSC) of multi-agent systems(MASs) defined over Laplacian dynamics on directed graphs. The agents that are divided into leaders and followers are connected based on the consensus law and only leaders are manipulated by the external control input directly. In contrast to existing work, the topology of MAS contains uncertain interconnection edges between agents. The interconnection graph has a zero/nonzero/arbitrary structure, to handle the parameters uncertainty problem in MASs. Under this framework, the authors propose a color-changing rule based on the zero-forcing set(ZFS). A graph-theoretic sufficient condition of SSC is proved. Next, the authors investigate the leader selection problem to ensure the SSC of MASs. A greedy algorithm based on ZFS is introduced. In addition, the authors figure out that the redundant property of edges in MASs can help us decide the leader selection problem. A new heuristic algorithm of polynomial complexity is developed to select minimum leaders of the multi-agent ***, the authors support the proposed analysis with numerical results on various simulations.

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Broad-Learning-system-Based Model-Free Adaptive Predictive control for Nonlinear MASs Under DoS Attacks

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IEEE/CAA Journal of Automatica Sinica 2025年第2期12卷 381-393页

作者： Hongxing Xiong Guangdeng Chen Hongru Ren Hongyi Li the School of Automation Guangdong-Hong Kong Joint Laboratory for Intelligent Decision and Cooperative Control Guangdong University of Technology Guangdong Provincial key laboratory for intelligent Decision and Cooperative control Guangdong University of Technology the College of Electronic and information Engineering Southwest University IEEE

In this paper, the containment control problem in nonlinear multi-agent systems(NMASs) under denial-of-service(DoS) attacks is addressed. Firstly, a prediction model is obtained using the broad learning technique to train historical data generated by the system offline without DoS attacks. Secondly, the dynamic linearization method is used to obtain the equivalent linearization model of NMASs. Then, a novel model-free adaptive predictive control(MFAPC) framework based on historical and online data generated by the system is proposed, which combines the trained prediction model with the model-free adaptive control method. The development of the MFAPC method motivates a much simpler robust predictive control solution that is convenient to use in the case of DoS attacks. Meanwhile, the MFAPC algorithm provides a unified predictive framework for solving consensus tracking and containment control problems. The boundedness of the containment error can be proven by using the contraction mapping principle and the mathematical induction method. Finally, the proposed MFAPC is assessed through comparative experiments.

关键词： Broad learning technique denial-of-service (DoS) model-free adaptive predictive control (MFAPC) nonlinear multi-agent systems (NMASs)

Online Fault-Tolerant Tracking control With Adaptive Critic for Nonaffine Nonlinear systems

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IEEE/CAA Journal of Automatica Sinica 2025年第1期12卷 215-227页

作者： Ding Wang Lingzhi Hu Xiaoli Li Junfei Qiao IEEE the School of information Science and Technology Beijing Key Laboratory of Computational Intelligence and Intelligent SystemBeijing Laboratory of Smart Environmental Protection and Beijing Institute of Artificial Intelligence Beijing University of Technology

In this paper, a fault-tolerant-based online critic learning algorithm is developed to solve the optimal tracking control issue for nonaffine nonlinear systems with actuator ***, a novel augmented plant is constructed by fusing the system state and the reference trajectory, which aims to transform the optimal fault-tolerant tracking control design with actuator faults into the optimal regulation problem of the conventional nonlinear error system. Subsequently, in order to ensure the normal execution of the online learning algorithm, a stability criterion condition is created to obtain an initial admissible tracking policy. Then, the constructed model neural network(NN) is pretrained to recognize the system dynamics and calculate trajectory control. The critic and action NNs are constructed to output the approximate cost function and approximate tracking control,respectively. The Hamilton-Jacobi-Bellman equation of the error system is solved online through the action-critic framework. In theoretical analysis, it is proved that all concerned signals are uniformly ultimately bounded according to the Lyapunov *** tracking control law can approach the optimal tracking control within a finite approximation error. Finally, two experimental examples are conducted to indicate the effectiveness and superiority of the developed fault-tolerant tracking control scheme.

关键词： Adaptive critic control neural network (NN) nonaffine nonlinear systems online fault-tolerant tracking design uniform ultimate boundedness

Semi-Autonomous Navigation Based on Local Semantic Map for Mobile Robot

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Journal of Shanghai Jiaotong university(Science) 2025年第1期30卷 27-33页

作者： ZHAO Yanfei XIAO Peng WANG Jingchuan GUO Rui Department of Automation Institute of Medical RoboticsShanghai Jiao Tong UniversityShanghai200240China State key laboratory of system control and information processing Ministry of Education of ChinaShanghai200240China Shanghai Engineering Research Center of intelligent control and Management Shanghai200240China State Grid Intelligence Technology Co. Ltd.Jinan250013China

Mobile robots represented by smart wheelchairs can assist elderly people with mobility *** paper proposes a multi-mode semi-autonomous navigation system based on a local semantic map for mobile robots,which can assist users to implement accurate navigation(e.g.,docking)in the environment without prior *** order to overcome the problem of repeated oscillations during the docking of traditional local path planning algorithms,this paper adopts a mode-switching method and uses feedback control to perform docking when approaching semantic *** last,comparative experiments were carried out in the real *** show that our method is superior in terms of safety,comfort and docking accuracy.

关键词： semi-autonomous navigation mobile robot semantic map voice interaction

Distributed optimal consensus control for multiagent systems based on event-triggered and prioritized experience replay strategies

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Science China(information Sciences) 2025年第1期68卷 306-321页

作者： Cuijuan ZHANG Lianghao JI Shasha YANG Xing GUO Huaqing LI Chongqing key laboratory of Image Cognition Chongqing University of Posts and Telecommunications key laboratory of intelligent Perception and Computing of Anhui Province Anqing Normal University College of Electronic and information Engineering Southwest University

This study uses event-triggered(ET) and reinforcement learning methods to investigate the optimal consensus control problem for cooperative-competitive multiagent systems. It proposes a novel distributed ET control strategy, which relies on a prioritized experience replay(PER) policy. This strategy not only conserves communication resources but also ensures acceptable system performance. To implement the proposed method, actor-critic(AC) dual-structured neural networks(NNs) are used to approximate the value function and control policy. In the AC NNs, the weight estimates for the NNs are updated at the moment of event triggering, resulting in a nonperiodic weight adjustment pattern. This approach decreases the computational cost in comparison with the traditional ET mechanism. The PER-based ET mechanism makes full use of valid historical data and effectively establishes a balance between system performance and communication resource ***, it does not require the following two conditions in most existing studies:(1) requirement of the system dynamics model to be known, and(2) persistent excitation. In addition, Zeno behavior is excluded from this study. Finally, a simulation is conducted to confirm the validity of the suggested approach.

关键词： multiagent systems event-triggered mechanism prioritized experience replay dual-structured neural networks optimal consensus control

Neural-based prescribed-time consensus control for multiagent systems via dynamic memory event-triggered mechanism

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Science China Technological Sciences 2025年第3期68卷 221-231页

作者： Xiaohong ZHENG Hui MA Qi ZHOU Hongyi LI School of Automation Guangdong-Hong Kong Joint Laboratory for Intelligent Decision and Cooperative Control and Guangdong Provincial Key Laboratory for Intelligent Decision and Cooperative Control Guangdong University of Technology School of Mathematics and Statistics Guangdong University of Technology College of Electronic and information Engineering and the Chongqing key laboratory of Generic Technology and system of Service Robots Southwest University

This work investigates the implementation of distributed prescribed-time neural network(NN) control for nonlinear multiagent systems(MASs) using a dynamic memory event-triggered mechanism(DMETM). First, it introduces a composite learning technique in NN control. This method leverages the prediction error within the NN update law to enhance the accuracy of the unknown nonlinearity estimation. Subsequently, by introducing a time-varying transformation, the study establishes a distributed prescribed-time control algorithm. The notable feature of this algorithm is its ability to predetermine the convergence time independently of initial conditions or control parameters. Moreover, the DMETM is established to reduce the actuation frequency of the controller. Unlike the conventional memoryless dynamic event-triggered mechanism, the DMETM incorporates a memory term to further increase triggering intervals. Utilizing a distributed estimator for the leader, the DMETM-based NN prescribed-time controller is designed in a fully distributed manner, which guarantees that all signals in the closed-loop system remain bounded within the prescribed time. Finally, simulation results are presented to validate the effectiveness of the proposed algorithm.

关键词： consensus control composite learning control dynamic memory event-triggered control prescribed-time control multiagent systems (MASs)