A DSC and MLP based robust adaptive NN tracking control for underwater vehicle

In this paper, a novel adaptive neural network (NN) controller is proposed for trajectory tracking of autonomous underwater vehicle (AUV). By employing radial basic function neural network to account for modeling errors, then the adaptive NN tracking controller is constructed by combining the dynamic surface control (DSC) and the minimal learning parameter (MLP). The proposed controller guarantees that all the close-loop signals are uniform ultimate bounded (UUB) 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.