Novel Dynamic Event-Triggered Consensus Control of Multiagent Systems With Markovian Switching Topologies Under DoS Attacks.

IF 9.4 1区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS IEEE Transactions on Cybernetics Pub Date : 2024-07-04 DOI:10.1109/TCYB.2024.3413167
Junyi Wang, Wenyuan He, Huaguang Zhang, Xiangpeng Xie, Yingchun Wang, Qinggang Meng
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Abstract

This article focuses on the issue of novel dynamic event-triggered consensus control of multiagent systems (MASs) with denial-of-service (DoS) attacks. Different from the conventional Markovian switching topologies, the generally uncertain semi-Markovian (GUSM) switching topologies with partially unknown elements and time-dependent uncertainties are constructed for the leader-following MASs by considering the equipment performance and external uncertain environment influence. To save communication resources, the novel dynamic memory event-triggered strategy (DMETS) is presented to decrease the frequency of communication between agents. Some secure consensus control criteria are established for the MASs with GUSM switching topologies and DoS attacks due to the potential system communication disruption caused by attackers. Finally, two physical system examples are designed to prove the effectiveness of the presented method.

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在 DoS 攻击下,具有马尔可夫开关拓扑的多代理系统的新颖动态事件触发共识控制。
本文重点讨论了具有拒绝服务(DoS)攻击的多代理系统(MAS)的新型动态事件触发共识控制问题。与传统的马尔可夫开关拓扑不同,本文通过考虑设备性能和外部不确定环境的影响,为领导跟随型 MAS 构建了具有部分未知元素和随时间变化的不确定性的一般不确定半马尔可夫(GUSM)开关拓扑。为了节省通信资源,提出了新颖的动态记忆事件触发策略(DMETS),以减少代理之间的通信频率。由于攻击者可能会造成系统通信中断,因此为具有 GUSM 交换拓扑结构和 DoS 攻击的 MAS 建立了一些安全共识控制标准。最后,设计了两个物理系统实例来证明所提出方法的有效性。
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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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