This paper presents an on-line trained neural net work controller for ship track-keeping problems. Following a brief review of the ship track-keeping control development since the 1980's, an analysis of various existing backpropagation-based neural controllers is provided. We then propose a single-input multioutput (SIMO) neural control strategy for situations where the exact mathematical dynamics of the ship are not available. The aim of this study is to build an autonomous neural controller which uses rudder to regulate both the tracking error and heading error. During the whole control process, the proposed SIMO neural controller adapts itself on-line from a direct evaluation of the control accuracy, and hence the need for a "teacher" or an off-line training process can be removed. With a relatively modest amount of quantitative knowledge of the ship behavior, the design philosophy enables real time control of a nonlinear ship model under random wind disturbances and measurement noise. Three different track-keeping tasks have been simulated to demonstrate the effectiveness of the training method and the robust performance of the proposed neural control strategy.


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    Title :

    A neural network approach to ship track-keeping control


    Additional title:

    Lernende robuste Regelung zur Kursbeibehaltung von Schiffen. Online trainierbare Kursregelung durch ein neuronales Netz


    Contributors:
    Zhang Yao (author) / Hearn, G.E. (author) / Sen, P. (author)

    Published in:

    Publication date :

    1996


    Size :

    15 Seiten, 20 Quellen




    Type of media :

    Article (Journal)


    Type of material :

    Print


    Language :

    English




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