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Volume 8 Issue 3
Mar.  2021

IEEE/CAA Journal of Automatica Sinica

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Giuseppe Franzè, Francesco Tedesco and Domenico Famularo, "Resilience Against Replay Attacks: A Distributed Model Predictive Control Scheme for Networked Multi-Agent Systems," IEEE/CAA J. Autom. Sinica, vol. 8, no. 3, pp. 628-640, Mar. 2021. doi: 10.1109/JAS.2020.1003542
Citation: Giuseppe Franzè, Francesco Tedesco and Domenico Famularo, "Resilience Against Replay Attacks: A Distributed Model Predictive Control Scheme for Networked Multi-Agent Systems," IEEE/CAA J. Autom. Sinica, vol. 8, no. 3, pp. 628-640, Mar. 2021. doi: 10.1109/JAS.2020.1003542

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

doi: 10.1109/JAS.2020.1003542
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  • 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.


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