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Erschienen in:
2013 | OriginalPaper | Buchkapitel
DSC Approach to Adaptive NN Control on a Ship Linear-Path Following
verfasst von : Wei Li, Zhihui Li, Jun Ning
Erschienen in: Advances in Neural Networks – ISNN 2013
Verlag: Springer Berlin Heidelberg
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The problem of ship linear path-keeping control is discussed. By employing radial based function neural network (RBF NN) to approximate uncertain nonlinear system functions, and by combining dynamic surface control (DSC) with backstepping technique and Nussbaum gain approach, the algorithm can not only overcome both the “explosion of complexity” problem inherent in the backstepping method and the possible “controller singularity” problem, but also reduce dramatically the number of on-line learning parameters, thus the algorithm can reduce the computation load of the algorithm correspondingly and make it easy in actual implementation. The stability analysis shows that all closed-loop signals will be semi-global uniformly ultimately bounded (SGUUB), when the tracking error converge to a small neighborhood around the origin through appropriately choosing design constants. Finally, simulation results are presented to show the effectiveness of the proposed algorithm.