An adaptive sliding mode control strategy for the heading control of autonomous underwater vehicles

Abstract

An adaptive sliding mode control strategy based on radial basis function (RBF) neural network was proposed to solve the heading control problem of autonomous underwater vehicle (AUV) under unknown parameters and external environment disturbance. Firstly, a nonlinear robust controller is designed by using the sliding mode control method, and the RBF neural network control technology is used to compensate the uncertain parameters of the model and the external nonlinear disturbance. Then, the stability of the whole closed-loop system is proved by using the Lyapunov stability theory. Finally, a numerical simulation is carried out for a new underwater hexapod crawling vehicle. The results show that the controller can realize accurate control of heading during variable speed navigation and effectively suppress chatterings caused by unknown model parameters.