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

• Published in: IEEE transactions on circuits and systems. I, Regular papers Vol. 68; no. 2; pp. 776 - 785
• Main Authors: Zhang, Wenle, Mao, Shuai, Huang, Jiahao, Kocarev, Ljupco, Tang, Yang
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.02.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Control methods; Control systems; Control theory; Cybersecurity; data-driven method; Discrete-time multi-agent network; Estimation; Frequency control; Heuristic algorithms; Multiagent systems; Protocols; resilient control; Switches; Tracking control; unconfined cyber-attacks; Viruses (medical)
• ISSN: 1549-8328; 1558-0806
• DOI: 10.1109/TCSI.2020.3037242

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.