Model Predictive Control with Disturbance Observer for Unmanned Surface Vessels Path Tracking

The underactuated unmanned-surface-vessel (USV) path tracking in a complex marine environment involves the minimization of the track error and the yaw angular velocity, while time-varying disturbances such as wind, waves and currents have to be compensated and controlled. To this end, the problem of path tracking is modeled through first-order nomoto model in the Serret-Frenet coordinate, and the disturbance observer is adopted to estimate the time-varying disturbances in real time. Then model predictive control (MPC) is used to compensate the disturbances, and the optimal control action is obtained by the optimization method effectively. Finally, the designed controller's stability is proved on the basis of the Lyapunov stability analysis method. Through simulation, it is shown that the designed controller is asymptotically stable in terms of track error deviation, heading angle deviation and heading angle speed, and its good stability and robustness in the presence of time-varving disturbances are verified.