Observer-Based Dynamic Event-Triggered Resilient Control for Heterogeneous Multi-Agent Systems Under DoS Attacks

多智能体系统 计算机科学 观察员(物理) 控制系统 控制理论(社会学) 控制(管理) 事件(粒子物理) 控制工程 工程类 人工智能 物理 量子力学 电气工程
作者
Yuan‐Cheng Sun,Zheng Zhang,Lina Yao,An‐Yang Lu
出处
期刊:IEEE Transactions on Automation Science and Engineering [Institute of Electrical and Electronics Engineers]
卷期号:22: 19723-19734 被引量:3
标识
DOI:10.1109/tase.2025.3598062
摘要

This paper studies the issue of dynamic event-triggered (ET) resilient control for heterogeneous multi-agent systems (MASs) under DoS attacks. Most of the existing work only considers ideal linear models and undirected graph communications. However, in practice, both disturbance and noise exist in the system model, and directed graph communication is more common. To solve this issue, a fully distributed dynamic ET control strategy with a prediction-based dynamic compensation algorithm is designed to deal with the difficulty of communication jamming. By using the Lyapunov method, it is proved that bounded consensus can be achievable in heterogeneous MASs under DoS attacks, and Zeno behavior is excluded. Furthermore, the tolerable attack intensity is quantified by attack frequency and duration. Finally, a numerical simulation is conducted to validate the efficacy of the proposed method.Note to Practitioners—In real-world scenarios, numerous complex tasks necessitate collaborative endeavors among heterogeneous intelligent agents, such as robots swarms, multiple vehicles and smart grids. Due to physical or geographical limitations, direct communication between all agents and a leader is not always feasible, thereby restricting individual agents to indirectly accessing the leader’s information. Furthermore, the practical constraints on network resources, coupled with the susceptibility to malicious DoS attacks, impede the completion of tasks by multiple agents. This paper presents a resilient dynamic ET control strategy for the consensus task of heterogeneous agents, aiming to improve system resilience while conserving network resources.
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