Objectives To address the issues of control channel interference and conservative controller design caused by separate sail-rudder control in waypoint tracking for unmanned sailboats, a deep learning-based joint sail-rudder model predictive control method is proposed.

Methods Firstly, the mathematical model of the unmanned sailboat's motion is established and the force conditions on the sail are analyzed. Then, a prediction model is constructed using a nonlinear state-space discretization method. The prediction model is identified online through a deep neural network, and multi-step prediction and output feedback correction techniques are employed to improve state prediction accuracy. Next, a composite objective function is formulated, integrating tracking error metrics and vessel speed metrics. Using cross-entropy optimization algorithms, the optimal control quantities for sail angle and rudder angle are solved within the prediction horizon, effectively overcoming the limitations of separated controller design. Finally, the PyTorch deep learning simulation platform was used for simulation.

Results The simulation results show that compared with the traditional separated PID control method for sails and rudder, the proposed method can significantly enhance the waypoint tracking performance of unmanned sailboats under dynamic changes in wind speed and direction, and shorten the overall completion time of the waypoint tracking task.

Conclusion This method can provide reliable theoretical support for unmanned sailing ships in the field of waypoint tracking control.