Distributed Model Predictive Contouring Control of Unmanned Surface Vessels

This article studies the cooperative path-following control problem for a group of unmanned surface vessels (USVs) which requires the vessels to track a predefined path while keeping their formation. A novel distributed model predictive contouring control (MPCC) method of USVs is proposed and an optimal cooperative control algorithm is designed. In particular, the proposed method penalizes the difference of contouring parameters between neighboring USVs to achieve distributed formation control, and optimizes the control inputs simultaneously in a receding horizon fashion. In this way, the MPCC method can balance the tracking accuracy and tracking speed to obtain a better formation quality than the traditional model predictive control following a time-dependent trajectory. Besides, an artificial potential field function is constructed to avoid collision in the receding horizon optimization. Finally, the comparison studies and hardware experiments are conducted to verify the effectiveness of the proposed method.