Intelligent ships at sea are influenced by environmental interference, and the influence of the uncertainty of model parameters leads to the problem of low ship motion control accuracy, so it is necessary to improve the resistance of the ship's control algorithm to such interference.

Based on an L1 adaptive control algorithm and Gaussian process regression (GPR) model, an L1 adaptive controller combined with a GPR model controller for underactuated ship path tracking control is proposed, and the control law is derived using the Lyapunov control function. The L1 adaptive controller is a new technique that considers both robustness and fast adaptivity. The closed-loop control system has proven to be consistently globally asymptotically stable, while the GPR model is used to model sudden disturbances and environmental disturbances during ship navigation, and achieve the rapid elimination of the effects of such disturbances in combination with the adaptive law.

The simulation results show that adding the GPR model reduces the average rudder amplitude by 14.9%, average absolute heading error by 23.2%, and maximum absolute heading error by 12.1%. The effects of environmental disturbances can be cancelled out faster and a stable state reached more rapidly than in cases without added disturbances.

The proposed L1-GPR adaptive controller can effectively resist various disturbances during navigation.