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Erschienen in:
2013 | OriginalPaper | Buchkapitel
Application-Oriented Adaptive Neural Networks Design for Ship’s Linear-Tracking Control
verfasst von : Wei Li, Jun Ning, Zhengjiang Liu
Erschienen in: Advances in Neural Networks – ISNN 2013
Verlag: Springer Berlin Heidelberg
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By employing Radial Basis Function (RBF) Neural Networks (NN) to approximate uncertain functions, an application-oriented adaptive neural networks design for ship linear-tracking control was brought in based on dynamic surface control (DSC) and minimal-learning-parameter (MLP) algorithm. With less learning parameters and reduced computation load, the proposed algorithm can avoid the possible controller singularity problem and the trouble caused by ”explosion of complexity” in traditional backstepping methods is removed, so it is convenient to be implemented in applications. In addition, the boundedness stability of the closed-loop system is guaranteed and the tracking error can be made arbitrarily small. Simulation results on ocean-going training ship ’YULONG’ are shown to validate the effectiveness and the performance of the proposed algorithm.