Resilience Against Replay Attacks: A Distributed Model Predictive Control Scheme for Networked Multi-Agent Systems

• Published in: IEEE/CAA journal of automatica sinica Vol. 8; no. 3; pp. 628 - 640
• Main Authors: Franze, Giuseppe, Tedesco, Francesco, Famularo, Domenico
• Format: Journal Article
• Language: English
• Published: Piscataway The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 01.03.2021; DIMES Department, University of Calabria, Arcavacata di Rende 87036, Italy
• Subjects: Algorithms; Cyberattack; Distributed model predictive control; Graph theory; leader-follower networks; Mathematical models; Multi-agent systems; Multiagent systems; Predictive control; replay attacks; Resilience; resilient control; Security; Topology; Trajectory; Distributed model predictive control; multi-agent systems; replay attacks; leader-follower networks; resilient control
• ISSN: 2329-9266; 2329-9274
• DOI: 10.1109/JAS.2020.1003542

In this paper, a resilient distributed control scheme against replay attacks for multi-agent networked systems subject to input and state constraints is proposed. The methodological starting point relies on a smart use of predictive arguments with a twofold aim: 1) Promptly detect malicious agent behaviors affecting normal system operations; 2) Apply specific control actions, based on predictive ideas, for mitigating as much as possible undesirable domino effects resulting from adversary operations. Specifically, the multi-agent system is topologically described by a leader-follower digraph characterized by a unique leader and set-theoretic receding horizon control ideas are exploited to develop a distributed algorithm capable to instantaneously recognize the attacked agent. Finally, numerical simulations are carried out to show benefits and effectiveness of the proposed approach.