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

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.

Automated summary

This paper proposes a resilient distributed control scheme against replay attacks for multi-agent networked systems, aiming to promptly detect and mitigate malicious agent behaviors using predictive arguments and set-theoretic receding horizon control ideas. The topological description of the multi-agent system by a leader-follower digraph and the development of a distributed algorithm capable of instantaneously recognizing attacked agents are key components of the proposed approach.