Consensus of nonlinear multi‐agent systems with adaptive protocols

Wang, Lei; Feng, Wei; Chen, Michael Z. Q.; Wang, Qingguo

doi:10.1049/iet-cta.2013.1081

Cited by 16 publications

(5 citation statements)

References 51 publications

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“…Using relative output measurements of neighboring agents, a distributed full‐order observer based consensus protocol is designed for MASs in Reference 8. Aiming at consensus of nonlinear MASs, an adaptive control protocol is developed via state feedback without global information in the work of Reference 9.…”

Section: Introductionmentioning

confidence: 99%

Efficient off‐policy Q‐learning for multi‐agent systems by solving dual games

Wang,

Xue,

Wen

et al. 2024

Intl J Robust & Nonlinear

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This article develops distributed optimal control policies via Q‐learning for multi‐agent systems (MASs) by solving dual games. According to game theory, first, the distributed consensus problem is formulated as a multi‐player non‐zero‐sum game, where each agent is viewed as a player focusing only on its local performance and the whole MAS achieves Nash equilibrium. Second, for each agent, the anti‐disturbance problem is formulated as a two‐player zero‐sum game, in which the control input and external disturbance are a pair of opponents. Specifically, (1) an offline data‐driven off‐policy for distributed tracking algorithm based on momentum policy gradient (MPG) is developed, which can effectively achieve consensus of MASs with guaranteed ‐bounded synchronization error. (2) An actor‐critic‐disturbance neural network is employed to implement the MPG algorithm and obtain optimal policies. Finally, numerical and practical simulation results are conducted to verify the effectiveness of the developed tracking policies via MPG algorithm.

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Section: Introductionmentioning

confidence: 99%

Efficient off‐policy Q‐learning for multi‐agent systems by solving dual games

Wang,

Xue,

Wen

et al. 2024

Intl J Robust & Nonlinear

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“…With the in‐depth research on the consensus issue, a multitude of results have emerged. To mention a few, the consensus problem for MASs with the nonlinear dynamic characteristics was addressed in Reference 10. An adaptive controller was designed to reach local and global consensus without using any global information.…”

Section: Introductionmentioning

confidence: 99%

Nonfragile consensus of nonlinear multiagent systems with intrinsic delays via aperiodic memory sampled‐data control

Han

Zhong

Wang

et al. 2022

Intl J Robust & Nonlinear

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This paper focuses on the consensus issue of nonlinear multiagent systems (MASs) with intrinsic time-varying delays via the nonfragile and aperiodic memory sampled-data control scheme. An improved continuous Lyapunov-Krasovskii functional (LKF) with some two-sided looped-functional terms is introduced, in which several free-weighting-matrices are not necessarily positive definite. Furthermore, based on the property of Laplacian matrix, the consensus issue of original MASs can be investigated directly without transforming into error systems. Then, several sufficient criteria for the consensus of MASs are presented by means of the recently developed generalized integral inequality and the constructed LKF. Next, the required nonfragile controller gain is obtained by resorting to linear matrix inequalities and the maximal length of sampling period is derived as well. Finally, two illustrative examples are employed to exhibit the effectiveness and feasibility of the proposed consensus protocol.

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“…Meanwhile, the formation problem of MASs is solved in Liu [16] using ILC. Wang et al [18] designed an adaptive consistency control law that didn't use global information to achieve local and global convergence of multi-agents. Chen et al [19] studied the consistency problem of general nonlinear coupled MASs and applied pinning control into the system, achieved good results.…”

Section: Introductionmentioning

confidence: 99%

Consensus of fractional‐order multi‐agent systems using iterative learning control in the sense of Lebesgue‐p norm

Lan

Dai

2021

Asian Journal of Control

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In this paper, open loop and closed loop PDα‐type fractional‐order iterative learning control algorithms with fixed topology for the fractional‐order distributed multi‐agent systems are proposed. In the sense of Lebesgue‐p norm, the convergences of the proposed methods are discussed by using the generalized Young inequality of convolution, and their convergence conditions are presented. The proposed algorithm extends the application scope of traditional iterative learning control theory. Theoretical analysis shows that by selecting the appropriate learning gain matrix, the output of each agent can completely track the desired trajectory in a limited time interval as the number of iterations increases. The feasibility and theoretical correctness of the presented algorithm are verified by numerical simulations.

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Consensus of nonlinear multi‐agent systems with adaptive protocols

Cited by 16 publications

References 51 publications

Efficient off‐policy Q‐learning for multi‐agent systems by solving dual games

Efficient off‐policy Q‐learning for multi‐agent systems by solving dual games

Nonfragile consensus of nonlinear multiagent systems with intrinsic delays via aperiodic memory sampled‐data control

Consensus of fractional‐order multi‐agent systems using iterative learning control in the sense of Lebesgue‐p norm

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