Predictive Fault-tolerant Control for Trajectory Tracking of Unmanned Surface Vehicle

Surface trajectory tracking and fault-tolerant control are of great significance to an unmanned surface vehicle (USV) system. In this paper, the trajectory tracking problem of a nonlinear USV with random noise is studied. A fault estimator based on extended Kalman Filter (EKF) is proposed for the multiplicative fault of the USV's actuators. In this paper, the system state and fault are jointly expanded, and then a minimum variance estimator is constructed based on EKF to estimate the original state and fault at the same time. Furthermore, in view of the multi-input multi-output problem of the USV, a controller based on model predictive control (MPC) is designed to make the USV complete trajectory tracking. In this process, the MPC controller uses the result of fault estimation to realize the function of fault-tolerant control. Finally, Matlab simulation is used to verify the accuracy of the proposed controller.