Trajectory Tracking of Unmanned Surface Vehicle Based on Nonlinear Model Predictive Control

Unmanned surface vehicles (USVs) are a type of intelligent marine robots operating in surface water environments, featuring high autonomy, exceptional maneuverability, and configurable modular design. These characteristics enable them to effectively support marine resource exploration and utilization. Precise trajectory tracking constitutes the fundamental requirement for USV autonomous navigation and represents a critical research focus in motion control domain. To address the trajectory tracking control challenge of USVs, this paper proposes a tracking controller based on Nonlinear Model Predictive Control (NMPC). A three- degree-of-freedom Lagrange motion model integrating both kinematic and dynamic characteristics is established, followed by the derivation of a nonlinear state-space representation. Within the NMPC framework, a trajectory tracking controller incorporating control input constraints is designed. The simulation experiments validate the controller's tracking performance through figure-eight trajectory tests and its disturbance rejection capability under environmental flow interference. The results demonstrate that the proposed controller achieves accurate trajectory tracking with mean position errors below 0.26 meters in simulated moving water conditions.