Online learning control of surface vessels for fine trajectory tracking

This paper presents an adaptive neural network (NN) controller for fine trajectory tracking of surface vessels with uncertain environmental disturbances. Regarding to the new demands for fine trajectory tracking, especially to the requirement of high-accuracy tracking in limited working space, the proposed NN controller is designed to contain a tracking error control component and a velocity error control component, aiming to converge both types of error to zero, separately. It utilizes radial basis functions to approximate a vessel's unknown nonlinear dynamics. Therefore, there is no need of any explicit knowledge of the vessel. The online learning ability is obtained during the stability analysis using the backstepping technique and the Lyapunov theory. Theoretical results guarantee both the convergence of tracking error and velocity error and the boundedness of NN update. Through simulation and tracking performance study based on the CyberShip II model, the proposed controller is verified effective in fine trajectory tracking.