Event-triggered leader-following consensus of multi-agent systems under semi-Markov switching topology with partially unknown rates

Huang, Wenchao; Tian, Binbin; Liu, Tiantian; Wang, Jinhua; Liu, Zhiqiang

doi:10.1016/j.jfranklin.2022.02.024

Cited by 17 publications

(8 citation statements)

References 38 publications

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“…For the multiagent system, the problem of parameter perturbation and external disturbances has been solved in Reference 30, which caused by measurement errors and noise etc. In addition, the coupling term between systems cannot be ignored, which has been treated as a nonlinear function to deal with in References 31‐33. In many studies of multiagent control methods, most of the controlled systems are assumed to be affine systems.…”

Section: Problem Formulation and Preliminariesmentioning

confidence: 99%

Reinforcement learning‐based adaptive consensus control for a class of non‐affine nonlinear multiagent systems with semi‐Markov switching topographies

Wang,

Liu

2023

Intl J Robust & Nonlinear

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A reinforcement learning‐based adaptive consensus control method has been proposed for the non‐affine nonlinear multiagent systems with semi‐Markov switching topographies in this paper. The considered system contains the nonlinear features, multiple source disturbances and non‐affine inputs, which makes it difficult to design the consensus controller. Firstly, to overcome the adverse influence caused by the multiple disturbances existing in the nonlinear system, a reinforcement learning based adaptive disturbance observer has been designed. Secondly, to handle the unknown nonlinear terms and the non‐affine features of the control inputs, an auxiliary loop has been introduced and a reinforcement learning based adaptive control law has been designed. Finally, aiming at the semi‐Markov switching topographies possessed by the multiagent systems, a novel adaptive consensus controller integrating the reinforcement learning approximator, the adaptive disturbance observer and the consensus control law, has been proposed. Through the Lyapunov direct method, it has been proven that the tracking errors are ultimately uniformly bounded. Moreover, within an acceptable margin of error, the state of all the followers can proved to be consistent with the leader. Several numerical experiments are conducted to illustrate the feasibility and effectiveness of the proposed reinforcement learning‐based leader‐following consensus control law.

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Section: Problem Formulation and Preliminariesmentioning

confidence: 99%

Reinforcement learning‐based adaptive consensus control for a class of non‐affine nonlinear multiagent systems with semi‐Markov switching topographies

Wang,

Liu

2023

Intl J Robust & Nonlinear

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“…Combined with switching topology, the leader-following consensus was presented in the research of He and Cai (2020). In the study of Huang et al (2022), the network topologies are randomly switched and governed by a semi-Markov process, the authors investigated the leader-following consensus problem for nonlinear multi-agent systems using event-triggered control. Considering switching topology and attacks, sufficient conditions for the consensus of multi-agent systems were obtained in the study of Wen et al (2020).…”

Section: Introductionmentioning

confidence: 99%

Impulsive bounded consensus of two-layer multi-agent networks under deception attacks

Gao,

Hu,

Jin

et al. 2024

Transactions of the Institute of Measurement and Control

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This paper investigates the mean-square bounded consensus issue in a two-layer multi-agent network under deception attacks. The two-layer network is composed of the leader and follower layers with a switching topology. Employing an impulsive control method, the mean-square bounded consensus for the leader layer and the node-to-node mean-square bounded consensus of the two-layer network are analyzed. Based on the knowledge of graph theory, Lyapunov stability theory, and linear matrix inequalities, sufficient conditions for the mean-square bounded consensus of multi-agent systems in the two-layer network are derived. Finally, the practicability and efficacy of the theoretical outcomes are corroborated via the provided numerical simulations.

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“…The H ∞ leader-following consensus problem in the mean square sense for nonlinear MASs under SMSTs is investigated [19]. Eventtriggered leader-following consensus of nonlinear MASs under SMSTs in the mean sense is researched [20]. Impulsive consensus of stochastic MASs under semi-Markovian switching topologies is researched [21].…”

Section: Introductionmentioning

confidence: 99%

“…For MASs under SMSTs, most of previous works [16]- [20] require the each possible topology contains a directed spanning tree (DST). In fact, some topologies perhaps do not have any DST.…”

Section: Introductionmentioning

confidence: 99%

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Almost Sure Exponential Consensus of Linear and Nonlinear Multi-Agent Systems Under Semi-Markovian Switching Topologies and Application to Synchronization of Chaos Systems

Xiao,

Wu,

Hou

et al. 2023

IEEE Access

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This paper researches the almost sure exponential leader-following consensus (ASELFC) of linear and nonlinear multi-agent systems (MASs) under semi-Markovian switching topologies (SMSTs). Differing from existing research works of MASs under SMSTs, we remove the restriction that every topology has a directed spanning tree (DST) rooted in the leader. Only partial switching topologies are requested to have a DST. By employing Lyapunov analysis method and stochastic technique, sufficient conditions for ASELFC are obtained. Finally, the theoretical results are applied to solve the synchronization of Chaos systems by using an example of Chua's circuits.INDEX TERMS Almost sure exponential leader-following consensus, semi-Markovian switching topologies, multi-agent systems.

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Event-triggered leader-following consensus of multi-agent systems under semi-Markov switching topology with partially unknown rates

Cited by 17 publications

References 38 publications

Reinforcement learning‐based adaptive consensus control for a class of non‐affine nonlinear multiagent systems with semi‐Markov switching topographies

Reinforcement learning‐based adaptive consensus control for a class of non‐affine nonlinear multiagent systems with semi‐Markov switching topographies

Impulsive bounded consensus of two-layer multi-agent networks under deception attacks

Almost Sure Exponential Consensus of Linear and Nonlinear Multi-Agent Systems Under Semi-Markovian Switching Topologies and Application to Synchronization of Chaos Systems

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