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15th IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Long, Jiang Wang, Wei Huang, Jiangshuai Wang, Lei Beihang Univ Sch Automat Sci & Elect Engn Beijing Peoples R China Beihang Univ Sch Automat Sci & Elect Engn Beijing Adv Innovat Ctr Big Data & Brain Comp Beijing Peoples R China Chongqing Univ Sch Automat Chongqing Peoples R China

ISBN: (数字)9781728151694

ISBN: (纸本)9781728151694

This paper investigates the distributed adaptive output consensus tracking problem for uncertain nonlinear multi-agent systems with event-triggered control inputs and event-triggered communication. A new event-based adaptive output consensus tracking scheme is proposed under directed graph condition. To compensate the effects of uncertain external disturbances and event-triggered measurement errors on the close-loop system stability, an additional parameter estimator is introduced in each agent to estimate the unknown lumped parameter consisting of the upper bound of external disturbances and the triggering threshold. Besides, both continuous monitoring of neighbors' states and the information of global parameters are not needed in designing the triggering mechanism. It is shown that all the signals in the closed-loop system are globally uniformly bounded and the desired output consensus tracking can be achieved.

关键词： distributed adaptive control uncertain nonlinear multi-agent systems event-triggered control

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Towards sample-efficient policy learning with DAC-ML

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Procedia Computer Science 2021年 190卷 256-262页

作者： Ismael T. Freire Adrián F. Amil Vasiliki Vouloutsi Paul F.M.J. Verschure Institute for Bioengineering of Catalonia (IBEC) Av. d’Eduard Maristany 10-14 08930 Barcelona Spain Universitat Pompeu Fabra (UPF) Barcelona Spain Barcelona Institue of Science and Technology (BIST) Barcelona Spain Institució Catalana de Recerca I Estudis Avançats (ICREA) Barcelona Spain

The sample-inefficiency problem in Artificial Intelligence refers to the inability of current Deep Reinforcement Learning models to optimize action policies within a small number of episodes. Recent studies have tried to overcome this limitation by adding memory systems and architectural biases to improve learning speed, such as in Episodic Reinforcement Learning. However, despite achieving incremental improvements, their performance is still not comparable to how humans learn behavioral policies. In this paper, we capitalize on the design principles of the distributed adaptive control (DAC) theory of mind and brain to build a novel cognitive architecture (DAC-ML) that, by incorporating a hippocampus-inspired sequential memory system, can rapidly converge to effective action policies that maximize reward acquisition in a challenging foraging task.

关键词： cognitive architecture sample-inefficiency problem sequence learning reinforcement learning distributed adaptive control

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distributed adaptive finite-time formation control of multiple UAV helicopter system

Distributed adaptive finite-time formation control of multip...

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第37届中国控制会议

作者： Dandan Wang Qun Zong Boyuan Zhang School of Electrical Engineering and Automation Tianjin University

This paper investigates distributed finite-time formation control(FFDFC) with local information change for multiple unmanned aerial vehicle(UAV) helicopter system with disturbances. The UAV helicopter system is composed by position outerloop and attitude inner-loop. We introduce an adaptive finite-time formation controller that takes into account motions of the information states. Unlike the existing distributed protocols, we show that the finite-time convergence properties is proved by Lyapunov theory. Moreover, the control algorithm removes the global information that needs to be known by each follower in advance in many references. Simulation results are presented to demonstrate the efficiency of the designed strategy.

关键词： Formation control Finite-time control distributed adaptive control UAV helicopters

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Bipartite consensus of matrix-weighted multi-agent systems with dynamic event-triggered mechanism: An adaptive observer-based approach

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Neurocomputing 2025年 646卷

作者： Xiaoli Zhu Yurong Liu College of Mathematics Yangzhou University Yangzhou 225002 China

This paper deals with the bipartite consensus issue for matrix-weighted nonlinear multi-agent systems (MASs). To accurately characterize the multi-dimensional attribute of state connections between agents, matrix weights are utilized to represent the connectivity relationships among agents instead of traditional scalar weights. Additionally, both collaborative and antagonistic interactions are considered on the matrix-weighted networks, which are inscribed with positive and negative semi-definite matrices, respectively. In the development of the distributed control protocol, observers are constructed to estimate the state of agents. Due to the fact that the full states of the nodes of a network are not always available, an observer-based distributed control protocol with matrix weights is constructed to achieve the desirable bipartite consensus. Meanwhile, to avoid using the global information of the communication network, dynamic coupling strengths are designed to obey an adaptive scheme. Also, a novel dynamic event-triggered mechanism is considered for the sake of reducing the communication burden. Based on the assumption that there exists a positive-negative spanning tree in matrix-weighted signed networks, employing algebraic graph theory and Lyapunov stability theory, sufficient conditions are derived to ensure that the matrix-weighted nonlinear MASs can achieve bipartite consensus. Moreover, it is demonstrated that the Zeno phenomenon can be ruled out under the proposed dynamic event-triggered mechanism. Finally, a simulation example is provided to verify the effectiveness of the proposed design method.

关键词： Bipartite consensus distributed adaptive control Dynamic event-triggered mechanism Matrix-weighted nonlinear multi-agent systems Observer-based control

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distributed adaptive sliding mode control for nonlinear heterogeneous multi-agent systems with unknown disturbances 36

Distributed adaptive sliding mode control for nonlinear hete...

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第36届中国控制会议

作者： ZHANG Xiang WANG Jinhuan WANG Jie YANG Dedong School of Sciences Hebei province key laboratory of big data calculationHebei University of Technology School of Control Science and Engineering Hebei University of Technology

ISBN: (纸本)9781538629185

In this paper,the consensus for a class of nonlinear heterogeneous multi-agent systems（MAS） subject to unknown external disturbances is ***,the neighbor-based distributed finite-time observers are proposed for the followers to estimate the position and velocity of the ***,a novel distributed adaptive control laws is designed by means of linear sliding mode（LSM）.One can prove that the tracking consensus can be realized asymptotically under LSM in spite of uncertainties and ***,a numerical example demonstrates the performance and feasibility of the given protocols.

关键词： multi-agent systems tracking consensus distributed adaptive control sliding mode

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Robust Neuro-adaptive Containment of Multileader Multiagent Systems With Uncertain Dynamics

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2019年第2期49卷 406-417页

作者： Wen, Guanghui Wang, Peijun Huang, Tingwen Yu, Wenwu Sun, Junyong Southeast Univ Sch Math Nanjing 210096 Jiangsu Peoples R China Texas A&M Univ Qatar Doha Qatar Peking Univ Coll Engn Beijing 100871 Peoples R China

One typical reflection of our understanding on multiagent systems (MASs) is our ability to design the emergence mechanism responsible for their various cooperative behaviors. This paper is concerned with the cooperative robust containment control problem of multileader MASs subject to unknown nonlinear dynamics and external disturbances. Specifically, quasi-containment and asymptotic containment problems are, respectively, considered by using tools from neural network (NN) approximation theory and Lyapunov stability theory of nonsmooth systems. A new kind of containment controllers consisting of a linear local information-based feedback term, a neuroadaptive approximation term and a nonsmooth feedback term are designed to complete the goal of quasi-containment. Under the assumption that the subgraph depicting the coupling configuration among followers is detail-balanced and each follower can be influenced by at least one leader, it is proven that the containment error vector of the closed-loop MASs will be uniformly ultimately bounded if the control parameters of the proposed containment controllers are suitably designed. By introducing a pseudo ideal weighting matrix for NN approximator embedded at each follower, a novel class of containment controllers are further designed to precisely achieve asymptotic containment in the considered MASs where the Euclidean norm of containment error vector asymptotically converges to zero. At last, numerical simulations are given to verify the validity of these derived theoretical results.

关键词： Communication topology containment control distributed adaptive control networking agent system neural network (NN) approximation

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Output Synchronization of Unknown Heterogeneous Agents via distributed Model Reference Adaptation

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IEEE TRANSACTIONS ON control OF NETWORK SYSTEMS 2019年第2期6卷 515-525页

作者： Baldi, Simone Yuan, Shuai Frasca, Paolo Delft Univ Technol Delft Ctr Syst & Control NL-2628 CD Delft Netherlands Harbin Inst Technol Sch Astronaut Harbin 150001 Heilongjiang Peoples R China Univ Grenoble Alpes Grenoble Inst Technol INRIA CNRS F-38400 St Martin Dheres France Univ Grenoble Alpes GIPSA Lab F-38400 St Martin Dheres France

This paper presents a distributed model reference adaptive methodology for the output synchronization of heterogeneous linear agents with unknown dynamics. We consider a setting in which the control input is communicated among neighbors, instead of observer variables. For those agents that can access the signals of the reference model, classical model reference adaptation laws lead to leader synchronization;for those agents that cannot access such signals, synchronization must be achieved by taking the neighboring agents as an alternative reference model. We show that these two groups of agents give rise to two types of matching conditions: the standard conditions to match the reference model, and new distributed matching conditions among neighboring agents. Since all matching gains are unknown, the gains are adapted online via the Lyapunov-based estimation. Asymptotic synchronization is proven analytically, and numerical examples show the effectiveness of the approach.

关键词： distributed adaptive control heterogeneous unknown agents output synchronization

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Neuro-adaptive cooperative tracking control with prescribed performance of unknown higher-order nonlinear multi-agent systems

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INTERNATIONAL JOURNAL OF control 2019年第2期92卷 445-460页

作者： Hashim, Hashim A. El-Ferik, Sami Lewis, Frank L. Univ Western Ontario Dept Elect & Comp Engn London ON Canada King Fahd Univ Petr & Minerals Dept Syst Engn Dhahran Saudi Arabia Univ Texas Arlington Dept Elect Engn UTA Res Inst Ft Worth TX USA

This paper is concerned with the design of a distributed cooperative synchronisation controller for a class of higher-order nonlinear multi-agent systems. The objective is to achieve synchronisation and satisfy a predefined time-based performance. Dynamics of the agents (also called the nodes) are assumed to be unknown to the controller and are estimated using neural networks. The proposed robust neuro-adaptive controller drives different states of nodes systematically to synchronise with the state of the leader node within the constraints of the prescribed performance. The nodes are connected through a weighted directed graph with a time-invariant topology. Only few nodes have access to the leader. Lyapunov-based stability proofs demonstrate that the multi-agent system is uniformly ultimately bounded stable. Highly nonlinear heterogeneous networked systems with uncertain parameters and external disturbances were used to validate the robustness and performance of the new novel approach. Simulation results considered two different examples: single-input single-output and multi-input multi-output, which demonstrate the effectiveness of the proposed controller.

关键词： Prescribed performance transformed error multi-agents neuro-adaptive distributed adaptive control consensus transient steady-state error

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distributed adaptive consensus tracking control of higher-order nonlinear strict-feedback multi-agent systems using neural networks

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NEUROCOMPUTING 2016年 214卷 269-279页

作者： Wang, Gang Wang, Chaoli Li, Lin Du, Qinghui Univ Shanghai Sci & Technol Shanghai 200093 Peoples R China Luoyang Normal Univ Dept Math Luoyang 471022 Peoples R China

This paper considers the output consensus problem of tracking a desired trajectory for a group of higher order nonlinear strict-feedback multi-agent systems over directed communication topologies. Only a subset of the agents is given direct access to the desired trajectory information. A distributed adaptive consensus protocol driving all agents to track the trajectory is presented using the backstepping technique and neural networks. The Lyapunov theory is applied to guarantee that all signals in the closed loop system are uniformly ultimately bounded and that all agents' outputs synchronize to the desired trajectory with bounded residual errors. Compared with prior work, the dynamics of each agent discussed here is more general and does not require the assumption "linearity in the unknown parameters" or the matching condition. Moreover, the bounded residual errors can be reduced as small as desired by appropriately choosing design parameters. Simulation results are included to demonstrate the effectiveness of the proposed methods. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Output consensus distributed adaptive control Backstepping control Neural networks Nonlinear systems

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Hierarchical Decomposition Based Consensus Tracking for Uncertain Interconnected Systems via distributed adaptive Output Feedback control

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IEEE TRANSACTIONS ON AUTOMATIC control 2016年第7期61卷 1938-1945页

作者： Wang, Wei Wen, Changyun Huang, Jiangshuai Li, Zhengguo Beihang Univ Sch Automat Sci & Elect Engn Beijing 100191 Peoples R China Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore Natl Univ Singapore Dept Elect & Comp Engn Singapore 117576 Singapore Inst Infocomm Res Signal Proc Dept Singapore 138632 Singapore

In this note, distributed adaptive controllers are developed for output consensus tracking of multiple linear systems with unknown parameters, uncertain subsystem interconnections and external disturbances. The subsystems are allowed to have non-identical dynamics and the same yet arbitrary order. It is assumed that only part of subsystems can have direct access to the time-varying trajectory information and the subsystem states are unmeasurable for local feedback control. In our design, the directed graph representing the information transmission status among subsystems is preprocessed by splitting it into a hierarchical structure. Then local adaptive controllers of subsystems in different layers can be computed in a sequential order and the difficulty on deriving mutually dependent local controls in previous consensus works are successfully overcome. In each subsystem, additional estimates are introduced to account for the unknown parameters and states in its neighbors' dynamics. Besides, only the information of local outputs and inputs need be collected from the neighboring subsystems. Certain robust terms are added in distributed adaptive laws to mitigate the effects of uncertain subsystem interactions and disturbances. It is proved that with our scheme, all closed-loop signals can be ensured bounded when the strengths of uncertain subsystem interconnections are sufficiently weak. The tracking errors for the entire group of subsystems will converge to a compact set and the transient tracking error performance can be adjusted by appropriately choosing design parameters.

关键词： Consensus tracking distributed adaptive control output feedback uncertain subsystem interconnections unknown parameters

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