Model predictive controller design with disturbance observer for path following of unmanned surface vessel

A model predictive controller with disturbance observer is presented to force an automated unmanned surface vessel (USV) to follow a reference path with environment disturbance. A full-scale trials by fully instrumented USV is carried out to identify the dynamic model. The Serret-Frenet frame is used to define the tracking error, therefore the position tracking errors can be stabilized by stabilizing the state of the control model to zero. And the constrained control input of the considered system is solved by minimizing performance based on model predictive control (MPC). Simulation and experiments results are presented to validate the effectiveness of the proposed method.