Abstract
In this paper, we investigate the problem of linear tracking control for an underactuated surface ship with rudder actuator dynamics. By using Radial Basis Function (RBF) Neural Networks (NN) to approximate the uncertainties of the systems, the problem of singularity is avoided and the trouble caused by “explosion of complexity” in traditional backstepping methods is removed by taking advantage of dynamic surface control(DSC) technique. Also, it is proved that all the signals of the closed-loop system are uniformly ultimately bounded(UUB), and the tracking error converges to the neighborhood of zero. The simulation results on an ocean-going training ship ’YULONG’ are shown to validate the proposed algorithm.
This work was supported in part by the National Natural Science Foundation of China(No.51179019), the Natural Science Foundation of Liaoning Province (No. 20102012) and the Program for Liaoning Excellent Talents in University(LNET).
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Li, W., Ning, J., Liu, Z., Li, T. (2012). Adaptive Neural Networks Control on Ship’s Linear-Path Following. In: Huang, T., Zeng, Z., Li, C., Leung, C.S. (eds) Neural Information Processing. ICONIP 2012. Lecture Notes in Computer Science, vol 7667. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34500-5_50
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DOI: https://doi.org/10.1007/978-3-642-34500-5_50
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