Robust Kalman Filter Based Path-Tracking Control with Prescribed Performance for Autonomous Ground Vehicles under FDI Attack

This paper researches the path-tracking control of autonomous ground vehicles (AGVs) with prescribed performance constraints when the measurement is under Gaussian noise and false data injection (FDI) attack. The robust Kalman Filter with credibility mechanism is utilized to filter out the attacked state data. The controller is designed by incorporating the prescribed performance control (PPC) and backstepping approaches, based on a combined brushed tire model. To achieve the control objective with concerned issues, three contribution are presented: 1) To eliminate the effect of noise and FDI attack, the robust Kalman Filter considers the credibility of measurement in three methods, and obtains a more accurate estimation; 2) A modified prescribed performance function (PPF) is developed in the PPC design to stabilize the control outputs, and constrain them within the PPF boundaries, where the need for accurate initial conditions is eliminated; 3) A modified backstepping control approach is presented to reduce the computational effort. Finally, high-fidelity simulations have been conducted to verify the effectiveness of the proposed approach, where the superiority of the proposed approach is demonstrated by comparing with a traditional sliding mode control (SMC) law.