Leader-Following Consensus of MASs With Input Constraints: A Time-Varying Dynamic Event-Triggered Approach

IF 8.6 1区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2025-04-01 DOI:10.1109/TSMC.2025.3552416

Meilin Li;Tieshan Li;Kai Zhang

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引用次数: 0

Abstract

The leader-following consensus problem for input constrained multiagent systems is investigated in this article. First, the follower agents are divided into several clusters based on the connectivity of the communication topology. Then, a novel clustered time-varying dynamic event-triggered mechanism (ETM) with adjustable minimum interevent time (MIET) is designed for each cluster, such that the follower agents within the same cluster share a unique ETM and are not influenced by agents in other clusters. Next, an event-triggered-based bounded control protocol with a time-varying gain and a parameter scheduler is proposed for each follower. Finally, the leader-following consensus can be realized with a faster convergence rate and the frequency of controller updates can be significantly reduced. Obviously, with the proposed clustered time-varying dynamic ETM, the Zeno-free phenomenon is guaranteed and the MIET can be modified by adjusting only a design parameter. Simulation results validate the effectiveness of the designed control algorithm.

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具有输入约束的群体领导-跟随共识：时变动态事件触发方法

研究了输入受限多智能体系统的领导-跟随一致性问题。首先，根据通信拓扑的连通性将follower agent划分为多个簇；然后，为每个集群设计了一种具有可调最小事件间时间（MIET）的新颖时变动态事件触发机制（ETM），使同一集群内的跟随代理共享唯一的ETM，不受其他集群中的代理的影响。其次，提出了一种具有时变增益和参数调度的基于事件触发的有界控制协议。最后，能够以更快的收敛速度实现leader- follower共识，显著降低控制器更新的频率。显然，采用聚类时变动态ETM可以保证无芝诺现象，并且只需调整设计参数即可修改MIET。仿真结果验证了所设计控制算法的有效性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

IEEE Transactions on Systems Man Cybernetics-Systems AUTOMATION & CONTROL SYSTEMS-COMPUTER SCIENCE, CYBERNETICS

CiteScore

18.50

自引率

11.50%

发文量

812

审稿时长

6 months

期刊介绍： The IEEE Transactions on Systems, Man, and Cybernetics: Systems encompasses the fields of systems engineering, covering issue formulation, analysis, and modeling throughout the systems engineering lifecycle phases. It addresses decision-making, issue interpretation, systems management, processes, and various methods such as optimization, modeling, and simulation in the development and deployment of large systems.