Trajectory Tracking Multi-mode Predictive Control Based on Soft-switching for Unmanned Surface Vehicle

The unmanned surface vehicle (USV) plays a vital role in ocean exploration and utilization. Its primary tasks include navigating designated routes and safely avoiding obstacles in complex environments, ensuring efficient and secure arrival at destinations. This paper proposes a soft-switching-based multi-mode predictive control method. Specifically, A two-stage control model is defined to categorize the control modes, and a nonlinear model predictive controller (NMPC) embedding relevant obstacle avoidance constraints is developed. Then combined with NMPC framework, a sigmoid function is introduced to handle the multi-mode control problem. In addition, we apply the proposed algorithm successfully to the trajectory tracking control of USV. Simulation results show the strength and reliability of the proposed algorithm, which reduces the errors and improves the control accuracy effectively.