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

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.

I. Introduction

AUVs have been used in many fields because of their wide range of activities, good maneuverability, safety and concealment. Due to the tasks such as energy replenishment, sample recovery, data transmission and task downloading, AUV must be docked for recovery after a certain period of underwater activity. In order to satisfy the continuity of underwater tasks of AUV and ensure the concealment of underwater submarine, underwater recovery technology has become a research hotspot. During the AUV of returning to the mother ship, the speed and heading of the vehicle will change in a wide range, and the reliability of the measured speed is generally poor. It is difficult to obtain the exact value, so the speed is listed as unknown parameters. In order to make the system robust to model uncertainty and external disturbance, based on sliding mode variable structure control, this paper designed neural network learning mechanism to approach model parameters online, adjusted the output of the controller, and improved the heading control performance of AUV in the recovery process.