Asynchronous-Based Bipartite Triggered Control for Insecure Multi-Agent Systems

IF 3.2 3区计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS

International Journal of Robust and Nonlinear Control Pub Date : 2025-04-21 DOI:10.1002/rnc.8008

Qingcao Zhang, Qing An, Yin Chen, Housheng Su

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引用次数: 0

Abstract

This paper investigates the problem of memory-event-driven bipartite tracking consensus of delayed linear multi-agent systems (MASs) within insecure cooperative and antagonistic interaction networks under scaling attacks. The study aims to address a more challenging and practical scenario of asynchronous communication. An effective approach of asynchronous memory-event-triggered control (METC) is proposed to overcome the security consensus problem of asynchronous scaling attacks on MASs with heterogeneous time-varying input delays, which may result in either communication tampering or communication interruption on the interactive edge between agents. Furthermore, a distributed memory-based delayed controller is designed to ensure exponential bipartite tracking consensus for the closed-loop control system. Additionally, the memory-based control gains under networked attacks are obtained using the Lyapunov functional method and Halanay inequality, and the upper bound for the allowable delay is established in the system stability analysis. Finally, the proposed control strategy and theoretical results are validated by simulation examples.

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不安全多智能体系统的异步二部触发控制

研究了不安全合作与对抗交互网络中延迟线性多智能体系统（MASs）在缩放攻击下的记忆事件驱动二部跟踪一致性问题。该研究旨在解决一个更具挑战性和实用的异步通信场景。针对具有异构时变输入延迟的海量网络中存在的通信篡改或通信中断问题，提出了一种有效的异步内存事件触发控制（METC）方法。此外，为了保证闭环控制系统的指数二部跟踪一致性，设计了一种基于分布式存储的延迟控制器。利用Lyapunov泛函方法和Halanay不等式获得了网络攻击下基于内存的控制增益，并在系统稳定性分析中确定了系统允许时延的上界。最后，通过仿真实例验证了所提出的控制策略和理论结果。

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

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.