Research on adaptive motion control method of ship course quantization neural network
Studying the adaptive motion control method for ship class course can accelerate the resolution of the prob-lems of difficult heading tracking detection and poor control effectiveness of ships under the limited communication band-width at sea.Based on the RBF neural network,a classical nonlinear motion analysis model is used to describe the quantiza-tion process of communication signal input,infinitely approximating the unknown nonlinear term in the heading control sys-tem to eliminate the influence of implicit uncertainty factor on the control system.At the same time,the RBF adaptive quant-ization controller designed in the model without needing to process prior information with quantization parameters,which not only ensures effective tracking and control,but also can reduce the transmission burden of communication,decrease exe-cution frequency and diminish system control amplitude.The effectiveness of the designed motion control method is veri-fied by constructing a simulation model in the environment of Matlab Simulink.
adaptive motion control methodRBF neural networkship course heading controlquantitative con-trolmotion analysis model
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