Neural network based robust adaptive tracking control for the automomous underwater vehicle

In this paper, robust adaptive tracking control is proposed for the autonomous underwater vehicle (AUV) in the presence of external disturbance. Backstepping control of the system dynamics is introduced to develop full state feedback tracking control. Using backstepping control, minimal learning parameter (MLP) and variable structure control (VSC) based techniques, the robust adaptive tracking control is presented for AUV to handle the uncertainties and improve the robustness. The proposed controller guarantees that all the close-loop signals are semi-global uniform boundedness and that the tracking errors converge to a small neighborhood of the desired trajectory. Finally, simulation studies are given to illustrate the effectiveness of the proposed algorithm.