Backstepping method tracking control for underactuated AUV with unknown dynamics based on action-critic networks based ADP

This work investigates trajectory-tacking control problem for underactuated autonomous underwater vehicles (AUV) with unknown dynamics. Due to the unknown dynamics, an action-critic networks based adaptive dynamic programming (ADP) scheme combined with backstepping approach is designed, which can achieve high-level system stability and tracking control accuracy. Firstly, the backstepping approach is introduced into the kinematic model of underactuated AUV and produces a virtual velocity control which is taken as the desired velocity input of the dynamic model of underactuated AUV. Secondly, the error tracking system is constructed according to the dynamic model of underactuated AUV. Thirdly, the critic neural network and the action neural network are employed to transform the trajectory-tracking control problem into optimal control problem based on policy iteration algorithm. At last simulation results are given to verify the effectiveness of the proposed control scheme.