Resilient and constrained consensus against adversarial attacks: A distributed MPC framework

Abstract

This paper proposes a distributed resilient consensus framework consisting of a distributed model predictive control (DMPC)-based consensus protocol and a new distributed attack detection mechanism, aiming to effectively handle the general linear constrained MAS under adversarial attacks. Based on the historical information from neighbors and a resilience set, we can evaluate the reliability of communication links via the attack detection mechanism. The proposed detection mechanism can significantly reduce the requirement on the network robustness compared with the well-known mean-subsequence-reduced (MSR) algorithms. The resilient consensus of general linear constrained MAS with F-locally adversarial attacks is achieved when the communication network is (F+1)-robust. Furthermore, the DMPC-based resilient consensus framework exhibits the following key feature: the constrained consensus performance is optimized by minimizing a set of control variables. We demonstrate that the recursive feasibility of the associated DMPC optimization problem and the resilient consensus convergence of the constrained MAS can be guaranteed. Finally, the effectiveness of the proposed framework is illustrated through simulation experiments.