A novel approach to robust adaptive NN tracking control for AUVs

In this paper, a novel adaptive neural network (NN) controller is proposed for trajectory tracking of autonomous underwater vehicles (AUVs) in the presence of model errors and external disturbance. A command filtered technique is used to tackle the problem of “explosion of complexity” inherent in the conventional backstepping method. Furthermore, the norm of the ideal weighting vector in neural network systems is considered as the estimation parameter, such that only one parameter is adjusted. It is also shown that the proposed NN based adaptive robust controller can guarantee the uniformly ultimately bounded of the AUV systems. Finally a numerical example is given to demonstrate the validity of the results.