Adaptive neural sliding mode trajectory tracking control for autonomous underwater vehicle without thrust model

Abstract

This paper presents a novel adaptive neural sliding mode trajectory tracking controller for autonomous underwater vehicle (AUV). Different from most existing AUVs' trajectory tracking methods, in this paper, the thruster control signal is considered as the input of trajectory tracking system directly. Based on the radial basis function (RBF) neural networks, the adaptive sliding mode trajectory tracking control law is designed. The stability analysis of trajectory tracking control system is given by Lyapunov theorem. The effectiveness of the proposed control scheme is illustrated by simulations.