Adaptive Dynamic Model-Based Path Following Controller Design for an Unmanned Surface Vessel

Abstract

The effective design of a path-following controller for unmanned surface vessels (USVs) under uncertain influences induced by various factors such as environmental disturbances is a challenging task. In this study, we propose to fulfill this task through taking benefits from an online parameter identification technique, the discrete-time sliding mode control (DSMC) method, and the improved line of sight (LOS) algorithm. The Particle Swarm Optimization algorithm (PSO) was adopted to provide initial settings for the straightforward online identification method, i.e., the Forgetting Factor Recursive Least Square method (FFRLS). In order to handle the time-varying sideslip angle of a ship that exists in reality due to environmental disturbances, a multimodel course control scheme is proposed to improve the control performance. For this control scheme, a flexible selection mechanism in between a heading angle or a course angle tracking controller based on the DSMC method is designed. A solution to fixing the tracking deviation problem of the LOS guidance law is investigated for which the gradient descent method is introduced. A series of experiments are carried out at sea with a USV called Orca to verify and validate the proposed hybrid path following approach. The results showed that tracking errors mainly induced by environmental disturbances existed but the maximum magnitude among them was small enough and remained within the acceptable range, especially from the marine engineering point of view. These results, to a high degree, validated the robustness and precision of the proposed controller.