Fully Distributed Observer-Based Leader-Following Consensus of Linear Multiagent Systems by Adaptive Dynamic Event-Triggered Schemes

IF 9.4 1区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

IEEE Transactions on Cybernetics Pub Date : 2024-11-11 DOI:10.1109/TCYB.2024.3485986

Lili Wang;Shiming Chen

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引用次数: 0

Abstract

The leader-following consensus issue for general linear multiagent systems (MASs) is investigated under event-triggered communication (ETC). Based on the output measurement of agent, two novel adaptive dynamic event-triggered (ADET) strategies for synchronous and asynchronous ETC are proposed, in which adaptive triggering parameters and time-varying threshold are introduced. Simultaneously, the corresponding control protocols are developed under the ADET strategies. Different from most existing relevant results, triggering mechanisms and control protocols do not require global information and network size, ensuring a fully distributed implementation. The asynchronous ETC, in particular, gives a flexible for transmitting information. The effectiveness of ADET strategies is further analyzed and discussed, and a comparative analysis between observer-based and state-based ADET algorithms is provided, highlighting their efficiencies and applications. Theoretical claims are substantiated with a simulative example that demonstrates the practical effectiveness of the proposed strategies.

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通过自适应动态事件触发方案实现线性多代理系统的全分布式基于观测器的领导者-跟随共识

研究了事件触发通信（ETC）下一般线性多智能体系统（MASs）的领导-跟随共识问题。在agent输出测量的基础上，提出了同步和异步ETC的自适应动态事件触发（ADET）策略，引入了自适应触发参数和时变阈值。同时，根据ADET策略制定了相应的控制协议。与大多数现有的相关结果不同，触发机制和控制协议不需要全局信息和网络规模，从而确保了完全分布式的实现。特别是异步ETC，为信息传输提供了灵活性。进一步分析和讨论了ADET策略的有效性，并对基于观测器和基于状态的ADET算法进行了比较分析，突出了它们的效率和应用。通过仿真实例验证了所提策略的实际有效性。

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 Cybernetics COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-COMPUTER SCIENCE, CYBERNETICS

CiteScore

25.40

自引率

11.00%

发文量

1869

期刊介绍： The scope of the IEEE Transactions on Cybernetics includes computational approaches to the field of cybernetics. Specifically, the transactions welcomes papers on communication and control across machines or machine, human, and organizations. The scope includes such areas as computational intelligence, computer vision, neural networks, genetic algorithms, machine learning, fuzzy systems, cognitive systems, decision making, and robotics, to the extent that they contribute to the theme of cybernetics or demonstrate an application of cybernetics principles.