ObjectiveA robust adaptive course-keeping control algorithm is designed to deal with the course-keeping problem for under-actuated ships with rudder faults, gain uncertainty and marine disturbances. MethodsBy combining the robust neural damping technique and adaptive approach, numerous neural network (NN) weights can be compressed horizontally, and only two gain-related adaptive learning parameters need to be designed to compensate for both the gain uncertainty and unknown fault parameters. The proposed controller is proven to be semi-global uniform and ultimately bounded (SGUUB) through Lyapunov analysis. Finally, the Nomoto mathematical model is established using "Yukun", and the effectiveness and superiority of the course-keeping algorithm is illustrated by carrying out comparison experiments under marine interference conditions. ResultsThe results show that the average rudder angle of "Yukun" under rudder failure is reduced by 51%, significantly improving control performance. ConclusionThe results of this study can provide references for tackling the course-keeping control problem of under-actuated ships.


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    Title :

    Robust adaptive course-keeping control of under-actuated ships with the rudder failure


    Contributors:
    Wenxin WANG (author) / Shang LIU (author) / Guoqing ZHANG (author) / Xianku ZHANG (author)


    Publication date :

    2023




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    Unknown