Dynamic Event-Triggered Fuzzy Adaptive Resilient Consensus Control for Nonlinear MASs Under DoS Attacks

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

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2024-08-09 DOI:10.1109/TSMC.2024.3426931

Jun Zhang;Yi Zuo;Shaocheng Tong

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引用次数: 0

Abstract

In this article, the adaptive fuzzy dynamic event-triggered output feedback resilient consensus control issue is investigated for nonlinear multiagent systems (MASs) subject to denial-of-service (DoS) attacks. Fuzzy logic systems (FLSs) are employed to model uncertain agents, and a state observer is constructed to estimate unmeasurable states. An event-triggered distributed resilient observer is designed to save the communication resources between agents, and estimate the unknown leader and its high-order derivatives in case of the communication topology being interrupted by DoS attacks. By the designed state observer and distributed resilient observer, a dynamic event-triggered resilient consensus control method is presented. It is proved that the controlled MASs are stable, and the followers can track the leader under DoS attacks. Moreover, the Zeno behavior can be excluded. Finally, we apply the developed resilient consensus control algorithm to multiple unmanned surface vehicles (USVs), the simulation results verify its effectiveness.

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DoS 攻击下非线性 MAS 的动态事件触发模糊自适应弹性共识控制

本文研究了受拒绝服务（DoS）攻击的非线性多代理系统（MAS）的自适应模糊动态事件触发输出反馈弹性共识控制问题。研究采用模糊逻辑系统（FLS）来模拟不确定的代理，并构建了一个状态观测器来估计不可测量的状态。设计了一个事件触发的分布式弹性观测器，以节省代理之间的通信资源，并在通信拓扑被 DoS 攻击中断时估计未知领导者及其高阶导数。通过所设计的状态观测器和分布式弹性观测器，提出了一种动态事件触发弹性共识控制方法。实验证明，受控的 MAS 是稳定的，在 DoS 攻击下，跟随者可以跟踪领导者。此外，还可以排除 Zeno 行为。最后，我们将所开发的弹性共识控制算法应用于多个无人水面飞行器（USV），仿真结果验证了该算法的有效性。

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.