RBFNN-Based Path Following Adaptive Control for Underactuated Surface Vessels

Abstract

A robust adaptive control strategy is developed to force an underactuated surface vessel to follow a reference path at a desired speed with the unknown parameters, despite the presence of environmental disturbances induced by wave, wind, and ocean current. The proposed controller is designed by using RBF neural networks and the backstepping techniques. The proposed control system allows for both low- and high-speed applications since linear and nonlinear damping terms were considered in the control design. Numerical simulation results are provided to demonstrate the effectiveness of the proposed controller design and the accuracy of stability analysis.