Resilient Output Control of Multiagent Systems With DoS Attacks and Actuator Faults: Fully Distributed Event-Triggered Approach

IF 10.5 1区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS IEEE Transactions on Cybernetics Pub Date : 2024-06-27 DOI:10.1109/TCYB.2024.3404010
Juan Zhang;Dongsheng Yang;Weihua Li;Huaguang Zhang;Guangdi Li;Peng Gu
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Abstract

This article investigates the fully distributed resilient practical leader-follower bipartite output consensus (LFBOC) problem for heterogeneous linear multiagent systems (MASs) with denial-of-service (DoS) attacks and actuator faults. To estimate the leader matrix and state in the presence of DoS attacks, two novel adaptive event-triggered observers are proposed based on newly developed lemmas, and then the adaptive event-triggered fault-tolerant controller without chattering behavior is developed to solve the LFBOC problem. Different from most existing resilient practical LFBOC working with DoS attacks and actuator faults, our method does not rely on any global information, event-triggered communication between neighbors and discrete update controllers are implemented simultaneously. Finally, an example is presented to well illustrate the effectiveness of developed method.
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具有 DoS 攻击和执行器故障的多代理系统的弹性输出控制:全分布式事件触发方法
本文研究了具有拒绝服务(DoS)攻击和执行器故障的异构线性多代理系统(MAS)的全分布式弹性实用领导者-跟随者双向输出共识(LFBOC)问题。为了估计存在 DoS 攻击时的领导者矩阵和状态,基于新开发的定理提出了两种新型自适应事件触发观测器,然后开发了无颤振行为的自适应事件触发容错控制器来解决 LFBOC 问题。与现有的大多数应对 DoS 攻击和执行器故障的弹性实用 LFBOC 不同,我们的方法不依赖于任何全局信息,邻域之间的事件触发通信和离散更新控制器是同时实现的。最后,我们通过一个示例充分说明了所开发方法的有效性。
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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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