Dynamic surface sliding mode algorithm based on approximation for three-dimensional trajectory tracking control of an AUV

Abstract

In this paper, a novel dynamic surface sliding mode control method is proposed for three-dimensional trajectory tracking control of autonomous underwater vehicle (AUV) in the presence of model errors. To enhance the robustness, the sliding mode control approach is modified by employing dynamic surface control (DSC). The radial basis function neural network (RBFNN) approximation technique is used for approximating model errors, furthermore the norm of the ideal weighting vector in neural network system is considered as the estimation parameter, such that only one parameter is adjusted. The proposed controller guarantees uniform ultimate boundedness (UUB) of all the signals in the closed-loop system via Lyapunov stability analysis, while the tracking errors converge to a small neighborhood of the desired trajectory. Finally, simulation studies are given to illustrate the performance of the proposed algorithm.