Path-following control of autonomous tugs based on Gaussian process regression and arithmetic optimization algorithm

Intelligent tugs with high autonomy are becoming a worldwide research focus, particularly for the needs of automated port operations. The path-following control of tug is an important part of intelligent tugs research. This paper proposes a path-following control method that combines Gaussian Process Regression (GPR) and Arithmetic Optimization Algorithm (AOA) for autonomous tugs. A GPR model is constructed to identify the correlation between the heading angle and the rotation angle of the azimuth thrusters, which acts as the prediction model in the path-following controller. The AOA is utilized to determine the optimal control inputs for each sampling interval, adopting the sine-cosine search strategy instead of addition and subtraction operations and introduces an inertia factor to improve the computational efficiency of AOA. A Line-of-Sight (LOS) algorithm is used as the guidance law to transform reference waypoints into reference heading angles, and the path-following controller is designed based on the GPR model and AOA. Simulation results show that the proposed method performs well in the path-following task without having prior knowledge regarding the hydrodynamic coefficients and ship parameters.