Abstract
In this paper, a novel robust adaptive neural control scheme is proposed for a class of ship course autopilot with input saturation.RBF neural networks (NNs) are used to tackle unknown nonlinear functions,then the robust adaptive NN tracking controller is constructed by combining dynamic surface control (DSC) technique and the minimal-learning-parameters (MLP) algorithm. The stability analysis subject to the effect of input saturation constrains is conducted employing an auxiliary design system. With only one learning parameter and reduced computation load, the proposed algorithm can avoid both problem of “explosion of complexity” in the conventional backstepping method and singularity problem. In addition, the boundedness stability of the closed-loop system is guaranteed and tracking error can be made arbitrary small. The effectiveness of the presented autopilot has been demonstrated in the simulation.
Published Version
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