文献类型：期刊文献

英文题名：Data-Driven Resilient Control for Linear Discrete-Time Multi-Agent Networks Under Unconfined Cyber-Attacks

作者：Zhang, Wenle[1];Mao, Shuai[1];Huang, Jiahao[1];Kocarev, Ljupco[2,3,4];Tang, Yang[1]

机构：[1]East China Univ Sci & Technol, Key Lab Adv Control & Optimizat Chem Proc, Minist Educ, Shanghai 200237, Peoples R China;[2]Macedonian Acad Sci & Arts, Skopje 1000, North Macedonia;[3]Univ Sv Kiril & Metodij, Fac Comp Sci & Engn, Skopje 1000, North Macedonia;[4]Univ Calif San Diego, BioCircuits Inst, La Jolla, CA 92093 USA

年份：2021

卷号：68

期号：2

起止页码：776

外文期刊名：IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS

收录：;EI(收录号：20204909594776);WOS:【SCI-EXPANDED(收录号:WOS:000608691300019)】；

基金：This work was supported in part by the National Natural Science Foundation of China under Grant 61806079, Grant 61751305, and Grant 61673176; in part by the Program of Introducing Talents of Discipline to Universities (the 111 Project) under Grant B17017; and in part by the Program of Shanghai Academic Research Leader under Grant 20XD1401300. This article was recommended by Associate Editor T. Fernando.

语种：英文

外文关键词：Discrete-time multi-agent network; resilient control; unconfined cyber-attacks; data-driven method

摘要：In this paper, the resilient control for linear discrete-time multi-agent networks subjected to unconfined cyber-attacks is investigated based on a data-driven method. Firstly, according to the evolution of the original network dynamics, a distributed data-driven estimation algorithm is presented. On this basis, a switching control law is proposed to solve the resilient consensus problem for the discrete-time multi-agent network under unconfined cyber-attacks. Further, some necessary and sufficient conditions for designing the resilient controllers are obtained by solving a nonlinear matrix inequation. Secondly, the proposed data-driven method is extended to study the resilient tracking control and formation control problems. Finally, some numerical simulations are provided to verify the effectiveness of the data-driven resilient control method.