独立 DoS 攻击下多代理系统的分布式事件触发共识控制:随机传输方法

IF 3.2 3区 计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS
Yuan-Cheng Sun, Lina Yao, Feisheng Yang, An-Yang Lu
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引用次数: 0

摘要

本文研究了独立拒绝服务(DoS)攻击下受干扰多代理系统(MAS)的共识问题。相关攻击会独立干扰不同的信道,导致网络拓扑结构时变且未知。在这种情况下,提出了一种具有混合通信策略的动态事件触发控制方案来安排网络上的信息交互,并提出了一种基于分布式预测的控制算法来改善恢复能力。通过引入与不同连接模式相关的 Lyapunov 函数的衰减率,证明了系统在 DoS 攻击下的稳定性。与大多数现有的 DoS 攻击下的 MAS 研究相比,所提出的随机传输协议保护了可能在触发时间受到恶意攻击的触发机制,并量化了可容忍的攻击强度。最后,通过仿真证明了建议的策略是有效的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Distributed event-triggered consensus control for multi-agent systems under independent DoS attacks: A randomized transmission approach

In this article, the consensus problem for disturbed multi-agent systems (MASs) under independent denial-of-service (DoS) attacks is investigated. The concerned attacks can jam different channels independently, resulting in a time-varying and unknown network topology. In this situation, a dynamic event-triggered control scheme with a hybrid communication strategy is presented to schedule information interaction over the network, and a distributed prediction-based control algorithm is proposed to ameliorate the resilience. The system stability is proved in present of DoS attacks by introducing the decay rates of Lyapunov functions associated with different connectivity modes. In comparison with the most existing MASs studies under DoS attacks, the triggering mechanism that possibly subject to the malicious attacks executed at the triggering times is protected by the proposed randomized transmission protocol, and the tolerable attack intensities are quantified. Finally, simulations are presented to substantiate that the proposed strategy is effective.

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来源期刊
International Journal of Robust and Nonlinear Control
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.
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