Dynamic event-triggered leader-following bipartite consensus of second-order multi-agent systems under DoS attacks

IF 3.2 3区计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS

International Journal of Robust and Nonlinear Control Pub Date : 2024-08-04 DOI:10.1002/rnc.7543

Xiao Wang, Jian Liu, Yongbao Wu, Changyin Sun

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引用次数: 0

Abstract

This article investigates the bipartite secure consensus of the second-order leader-follower multi-agent systems (MASs) against aperiodic denial of service (DoS) attacks. First, a new dynamic event-triggered strategy is proposed for the consensus control. Compared with the static event-triggered strategy, the dynamic event-triggered protocol can reduce the trigger times and save resources by introducing an internal dynamic variable. Different from the secure consensus results which only discussed the cooperative links, based on the theory of the structurally balanced graph, this article considers cooperative and antagonistic interactions among agents simultaneously for complex behaviors of them. Besides, the characteristics of the DoS attacks are analyzed in detail and the secure consensus is achieved by utilizing the Lyapunov theory and inductive methods. Finally, the Zeno behavior is excluded and the availability of the designed algorithms is demonstrated by numerical simulations.

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DoS 攻击下二阶多代理系统的动态事件触发式领导者-跟随者双方共识

本文研究了二阶领导者-追随者多代理系统（MAS）的双方位安全共识，以对抗非周期性拒绝服务（DoS）攻击。首先，为共识控制提出了一种新的动态事件触发策略。与静态事件触发策略相比，动态事件触发协议通过引入内部动态变量，减少了触发时间，节省了资源。与只讨论合作链接的安全共识结果不同，本文基于结构平衡图理论，同时考虑了代理间的合作与对抗交互，以解决代理的复杂行为。此外，本文还详细分析了 DoS 攻击的特点，并利用 Lyapunov 理论和归纳法实现了安全共识。最后，排除了 Zeno 行为，并通过数值模拟证明了所设计算法的可用性。

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

International Journal of Robust and Nonlinear Control 工程技术-工程：电子与电气

CiteScore

6.70

自引率

20.50%

发文量

505

审稿时长

2.7 months

期刊介绍： Papers that do not include an element of robust or nonlinear control and estimation theory will not be considered by the journal, and all papers will be expected to include significant novel content. The focus of the journal is on model based control design approaches rather than heuristic or rule based methods. Papers on neural networks will have to be of exceptional novelty to be considered for the journal.