This paper deals with the problem of course keeping control for marine vessel with limited communication band-width, unknown environment interference and model uncertainty. Firstly, for the Nomoto nonlinear model of marine vessel, the Backstepping method is used to deduce the course-keeping controller. By using the event-triggered condition, the rudder angle command is transmitted only when the condition is met, which can reduce the steering frequency of the rudder equipment. By taking advantage of radial basis neural networks (RBF-NN), unknown system terms and external disturbances can be compressed into an unknown parameter, and only one adaptive law needs to be updated online. Through Lyapunov analysis, it is proved that all tracking errors of the closed-loop system are globally consistent and finally bounded. Finally, the effectiveness of the algorithm is verified by numerical simulation.


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    Title :

    Adaptive Neural Event-Based Course-Keeping Control of Marine Vessel


    Contributors:
    Zhao, Zhenqi (author) / Deng, Linjiang (author) / Yu, Wenduo (author) / Wang, Chu (author) / Wang, Xiaoyun (author) / Zhang, Xuyang (author)


    Publication date :

    2024-05-17


    Size :

    360508 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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