Resilience Analysis of Closed-Loop Multiagent Systems Under Replay Attacks in the Context of Consensus Tracking

At present, extensive research efforts have been devoted to investigating network attacks on control systems. However, comparatively limited attention has been directed toward the resilience of multiagent systems (MASs) under such attacks, particularly in the case of replay attacks. Addressing the challenge of resilience analysis in strict-feedback nonlinear MASs under replay attacks, this article proposes a dynamic stability analysis method based on a classical distributed adaptive consensus control framework. To evaluate the resilience of the MASs in the context of aperiodic replay attacks, a dynamic compact set model is designed as a resilience metric. An iterative algorithm is then developed to compute the upper bound of tracking error jump at the beginning and end of the attack. In the scenario where the control signals under replay attacks cannot be explicitly modeled, this study derives an upper bound on the variation of the tracking error during the attack period using the Lyapunov stability analysis. It is proven that resilience can be maintained when the resting time between two consecutive replay attacks satisfies a given sufficient condition. Finally, simulation results illustrate the effectiveness of the proposed analysis method.