Distributed Event-Triggered Fault-Tolerant Consensus Control of Multi-Agent Systems Under DoS Attacks

IF 3 3区计算机科学 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2024-04-03 DOI:10.1109/TSIPN.2024.3384814

Chun Liu;Bin Jiang;Yang Li;Ron J. Patton

引用次数: 0

Abstract

This study investigates the distributed fault-tolerant consensus issue of multi-agent systems subject to complicated abrupt and incipient time-varying actuator faults in physical hierarchy and aperiodic denial-of-service (DoS) attacks in networked hierarchy. Decentralized estimators are devised to estimate consecutive system states and actuator faults. A unified framework with an absolute local output-based closed-loop estimator in decentralized fault estimation design and a relative broadcasting state-based open-loop estimator in distributed event-triggered fault-tolerant consensus design is developed. Criteria of exponential consensus of the faulty multi-agent systems under DoS attacks are derived by virtue of average dwelling time and attack frequency technique. Simulations are outlined to confirm the efficacy of the proposed distributed fault-tolerant consensus control algorithm based on an event-triggered mechanism.

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多代理系统在 DoS 攻击下的分布式事件触发容错共识控制

本研究探讨了多代理系统的分布式容错共识问题，该系统在物理层次结构中受到复杂的突发性和萌芽期时变致动器故障的影响，在网络层次结构中受到非周期性拒绝服务（DoS）攻击的影响。我们设计了分散估计器来估计连续的系统状态和执行器故障。在分散式故障估计设计中，开发了基于绝对本地输出的闭环估计器；在分布式事件触发容错共识设计中，开发了基于相对广播状态的开环估计器。通过平均停留时间和攻击频率技术，得出了在 DoS 攻击下故障多代理系统的指数共识标准。仿真结果证实了基于事件触发机制的分布式容错共识控制算法的有效性。

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 Signal and Information Processing over Networks Computer Science-Computer Networks and Communications

CiteScore

5.80

自引率

12.50%

发文量

期刊介绍： The IEEE Transactions on Signal and Information Processing over Networks publishes high-quality papers that extend the classical notions of processing of signals defined over vector spaces (e.g. time and space) to processing of signals and information (data) defined over networks, potentially dynamically varying. In signal processing over networks, the topology of the network may define structural relationships in the data, or may constrain processing of the data. Topics include distributed algorithms for filtering, detection, estimation, adaptation and learning, model selection, data fusion, and diffusion or evolution of information over such networks, and applications of distributed signal processing.