Bipartite containment control of multi-agent systems under DoS attacks: an event-triggered scheme

IF 3.2 3区 计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS International Journal of Robust and Nonlinear Control Pub Date : 2024-10-04 DOI:10.1002/rnc.7660
Zihan Liu, Hao Zhang, Zhuping Wang
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Abstract

This paper investigates the problem of bipartite containment control for multi-agent systems (MASs) under denial-of-service (DoS) attacks with event-triggered scheme. The DoS attacks can cause disruptions to communication channels, suspending or crashing services. In order to minimize the impact of the attacks on the control process, this paper introduces a bipartite containment control protocol with a resilient event-triggered scheme, which enables the MASs not only to resist DoS attacks but also to optimize the utilization of resources and accomplish the control target. After considering the previous, the paper suggests a new observer to estimate the state of agents in order to cope with the unmeasurable state of MASs. Moreover, Zeno behavior avoidance is rendered more effective by the introduction of a positive constant in the event-triggered function. At last, simulation data and results are given, thus verifying the feasibility of solving the bipartite containment problem under DoS attacks.

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DoS攻击下多代理系统的两部分控制:一种事件触发方案
研究了基于事件触发方案的拒绝服务攻击下多智能体系统的双侧控制问题。DoS攻击会导致通信通道中断,服务暂停或崩溃。为了最大限度地减少攻击对控制过程的影响,本文引入了一种具有弹性的事件触发机制的双向控制协议,使MASs既能抵御DoS攻击,又能优化资源利用,实现控制目标。在此基础上,提出了一种新的观测器来估计agent的状态,以解决质量状态不可测的问题。此外,通过在事件触发函数中引入正常数,Zeno行为回避变得更加有效。最后给出了仿真数据和结果,验证了在DoS攻击下解决二部遏制问题的可行性。
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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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