MPC-Based Resilient Cooperative Target Tracking of Autonomous Surface Vehicles Under False Data Injection Attacks

This paper addresses the cooperative target tracking control problem for multiple tracking autonomous surface vehicles (ASVs) under false data injection (FDI) attacks from the target ASV. A model predictive control (MPC) - based resilient cooperative target tracking control scheme is proposed for mitigating the effect of the attacks and restoring the tracking performance. Specifically, a nominal cooperative target tracking control law is designed to achieve cooperative target tracking without FDI attacks where a finite-time extended state observer (ESO) is proposed to estimate the model uncertainties. Next, the FDI attacks from the target ASV are modeled by using a MPC method to hinder the cooperative tracking of multiple ASVs. Then, optimal resilient signals are developed via the MPC approach to counteract the attack effects and thereby recover cooperative tracking performance. The input-to-state stability (ISS) of the proposed finite-time ESO error dynamics is analyzed by employing a homogeneous Lyapunov function, besides in the entire closed-loop control system, all tracking error signals are uniformly ultimately bounded. The efficacy of the proposed resilient control method is confirmed through simulation results.