Event-Based Dynamic Quantized Control for Bipartite Consensus

IF 8.7 1区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2024-12-23 DOI:10.1109/TSMC.2024.3514697
Jie Wang;Yuchen Dong;Bailing Tian;Qun Zong
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Abstract

This article investigates secure bipartite consensus control of nonlinear multiagent systems (MASs) under denial-of-service (DoS) attacks, and designs an event-based dynamic quantized sliding mode control scheme. In order to ensure the stability of MASs in the process of achieving bipartite consensus, combined with the online adjustment strategy of quantitative sensitivity parameters and the designed event-triggered mechanism, the constraints of quantitative measurement saturation parameters and event-triggered threshold parameters are given. Moreover, it is proved that Zeno behavior does not occur at the zoom-out/zoom-in stage. Then, combined with reasonable assumptions about the frequency and duration of DoS attacks, appropriate controller parameters are redesigned, and the stability of the system is proved by Lyapunov stability theory and mathematical induction. Finally, the effectiveness of the proposed method is illustrated by a simulation example.
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基于事件的二部一致性动态量化控制
研究了拒绝服务攻击下非线性多智能体系统(MASs)的安全二部共识控制,设计了一种基于事件的动态量化滑模控制方案。为了保证质量在达成二方共识过程中的稳定性,结合定量灵敏度参数在线调整策略和所设计的事件触发机制,给出了定量测量饱和参数和事件触发阈值参数的约束条件。此外,还证明了在放大/缩小阶段不发生芝诺行为。然后,结合对DoS攻击频率和持续时间的合理假设,重新设计适当的控制器参数,并利用Lyapunov稳定性理论和数学归纳法证明了系统的稳定性。最后,通过仿真算例验证了该方法的有效性。
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来源期刊
IEEE Transactions on Systems Man Cybernetics-Systems
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.
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