异构 MAS 输出共识的修正动态事件触发机制

IF 9.4 1区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS
Wang Yang;Jiuxiang Dong
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引用次数: 0

摘要

本文通过一种改进的分布式动态事件触发控制设计来解决异构多智能体系统(MASs)的输出一致性问题。首先,在不使用全局信息的情况下,提出了一个完全分布式的异步事件触发补偿器作为虚拟参考生成器。其次,在此补偿器的基础上,为每个异构代理开发了分布式事件触发控制器,以达到输出一致性。具体而言,为了节省资源和保证渐近共识,提出了一种改进的动态事件触发机制(DETM),其中引入了时间触发机制(TTM)以确保存在正最小事件间时间,并设计了时间触发机制以进一步避免冗余触发动作。此外,DETM的触发条件不需要连续检测,只有当TTM中的定时器变量从最大值减少到零时才会检测。对所开发的控制方案的这些特点进行了分析和数值验证。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Modified Dynamic Event-Triggered Mechanism for Output Consensus of Heterogeneous MASs
This article addresses the output consensus problem of heterogeneous multiagent systems (MASs) via a distributed modified dynamic event-triggered control design. First, a fully distributed asynchronous event-triggered compensator for each agent is proposed to act as a virtual reference generator without using global information. Second, based on this compensator, a distributed event-triggered controller for each heterogeneous agent is developed to reach output consensus. Specifically, to save resources and guarantee asymptotic consensus, a modified dynamic event-triggered mechanism (DETM) is proposed, in which a time-triggered mechanism (TTM) is introduced to ensure the existence of a positive minimum interevent time and a DETM is designed to further avoid redundant triggering actions. Besides, the triggering condition of the DETM does not require continuous detection, it is only detected when the timer variable in the TTM deceases from maximum value to zero. These features of the developed control scheme are presented analytically and verified numerically.
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来源期刊
IEEE Transactions on Cybernetics
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.
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