Xiaomin Guo

To address the issues of model uncertainties and external disturbances in path tracking control, a gain-scheduled adaptive Tube-NMPC algorithm is proposed. Tube robust constraints are incorporated into the NMPC framework to enhance disturbance rejection capability, while a gain-scheduling mechanism is employed to achieve online adjustment of control parameters under different operating conditions. An adaptive update-rate strategy is further designed to dynamically regulate the tube contraction parameter, thereby improving the adaptability of the control system to environmental variations. Experiments are conducted under both disturbance-free conditions and disturbance scenarios involving surge force, sway force, and yaw moment disturbances. The results demonstrate that the proposed algorithm achieves superior performance in position tracking, velocity tracking, and control input smoothness. The effectiveness of the proposed method is validated, demonstrating improved path tracking accuracy and control stability under disturbance conditions.

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.

To address the issues of path tracking accuracy and control stability in autonomous ship berthing, an improved algorithm combining nonlinear model predictive control (NMPC) and convolutional neural networks (CNNs) is proposed in this paper. A CNN is employed to train on a large dataset of ship berthing trajectories, combined with the rolling optimization mechanism of NMPC. A high-precision path tracking control method is designed, which accounts for ship motion constraints and environmental disturbances. Simulation results show an 88.24% improvement in tracking precision over traditional MPC. This paper proposes an improved nonlinear model predictive control (NMPC) strategy for autonomous ship berthing. By integrating convolutional neural networks (CNNs) and moving horizon estimation (MHE), the method enhances robustness and path-tracking accuracy under environmental disturbances. The amount of system overshoot is reduced, and the anti-interference capability is notably improved. The effectiveness, generalization, and applicability of the proposed algorithm are verified.