Solving the problem of ship maneuvering through difficult passages has always been one of the main tasks in maritime and inland navigation. To improve the operational capacity of inland water traffic, it is necessary to develop more sophisticated approaches for controlling ship motions leading to enhanced control performance and reliability. This task can be solved on the one hand by improved controller parameterizations and, on the other hand, by the implementation of more efficient control structures. In this context, the application of neural controllers and their combination with classical approaches is advisable to obtain control approaches which can adapt itself to a-priori unknown situations. The final goal of this scientific research will be the implementation of the developed control algorithms on the onboard computers of a ship to provide assistance for captains during both steering in critical route segments and piloting a ship into ports. Another possible application is the usage in ship simulators, which serve as training facilities for captains to find solutions in the case of simulated emergency situations such as collision avoidance by evading oncoming vessels.


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    Titel :

    Development of a neural network-based controller for ships


    Beteiligte:


    Erscheinungsdatum :

    2010


    Format / Umfang :

    10 Seiten, 9 Bilder, 9 Quellen



    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Datenträger


    Sprache :

    Englisch





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