Autonomous berthing path tracking of a 4-DOF ship under nonlinear model predictive control

To address the path tracking and control difficulties faced by unmanned surface ship in severe maritime environments, this research introduces a nonlinear model predictive control (NMPC) based approach to attain intelligent and accurate berthing. This research proposes a 4-DOF(four-degree-of-freedom) ship berthing path tracking method based on NMPC. By combining moving horizon estimation (MHE), the author establishes the Fossen dynamic model of the ship in 4-DOF motion: sway, surge, yaw, and roll. Meanwhile, a nonlinear model predictive control system is designed to predict future motion trajectories and optimize control inputs in real-time, dynamically adapting to the ship's state and environmental variations, thereby enhancing the precision of berthing control. The autonomous berthing simulation experiment was simulated in the Port of Hamburg. The results show that the track error is less than 2 m, and the error in the berthing position is only 0.3 m. These results confirm the efficiency, generalization, and suitability of the proposed algorithm. This research aims to design a motion control system for ship berthing path tracking. It improves the attitude control of unmanned ships during berthing under rough sea conditions compared to traditional PID control and NMPC control. This provides a core theoretical basis, delivering essential support and practical recommendations to improve the reliability and performance of ship berthing procedures for autonomous intelligent ships.