Finite-time cooperative output regulation of heterogeneous nonlinear multi-agent systems under switching DoS attacks

被引:10
作者
Zhang, Dan [1 ]
Chen, Haoling [1 ]
Lu, Qun [1 ,2 ]
Deng, Chao [3 ]
Feng, Gang [4 ]
机构
[1] Zhejiang Univ Technol, Key Res Ctr Automat & Artificial Intelligence, Hangzhou 310023, Zhejiang, Peoples R China
[2] Taizhou Univ, Sch Aeronaut Engn, Jiaojiang 318000, Zhejiang, Peoples R China
[3] Nanjing Univ Posts & Telecommun, Inst Adv Technol, Nanjing 210023, Jiangsu, Peoples R China
[4] City Univ Hong Kong, Dept Biomed Engn, Hong Kong, Peoples R China
基金
中国国家自然科学基金;
关键词
Heterogeneous MASs; Nonlinear MASs; COR; Finite-time control; Adaptive control; DoS attacks; TRACKING CONTROL; DESIGN;
D O I
10.1016/j.automatica.2024.112062
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
This paper addresses the problem of finite-time cooperative output regulation (COR) for a class of heterogeneous nonlinear multi-agent systems (MASs) subject to switching denial-of-service (DoS) attacks. The communication topology of the MASs under DoS attacks would be time-varying and even completely disconnected during certain time intervals. A novel resilient distributed observer is first designed to obtain the information of the exosystem state, and it is proven that the observer errors converge to zero within a finite time when the bound of the total DoS duration satisfies certain conditions. Then based on this observer and the finite-time command filter back-stepping technique, a new adaptive finite time controller is proposed. A notable feature of our proposed controller is its ability to handle the non-differentiability of the observer state caused by the occurrence of DoS attacks, which is typically problematic in the conventional backstepping design. It is shown that the resulting closed-loop system achieves practical finite-time stability. Finally, we validate the effectiveness of the proposed control scheme through a numerical simulation study. (c) 2024 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.
引用
收藏
页数:12
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