研究船舶类航向自适应运动控制方法有助于加快解决船舶在海上通讯带宽受限情况下航向跟踪检测困难和控制效果差的问题。基于RBF神经网络，采用一种经典非线性运动解析模型来描述通信信号输入量化过程，无限逼近于航向控制系统中的未知非线性项来消除隐性不确定项因子对控制系统的影响，与此同时模型中所设计的RBF自适应量化控制器不需要对先验信息进行量化参数处理，不仅可以保证有效跟踪和控制的同时，还可以减轻通信的传输负担、减少执行频次和降低系统控制幅度。本文基于Lyapunov稳定性理论证明了所提出的带有输入量化的RBF神经网络自适应闭环控制系统的稳定性，并在Matlab Simulink环境中构建仿真模型分析，论证了所设计的运动控制方法的有效性。
Studying the adaptive motion control method for ship class course can accelerate the resolution of the problems of difficult heading tracking detection and poor control effectiveness of ships under the limited communication bandwidth at sea. Based on the RBF neural network, a classical nonlinear motion analysis model is used to describe the quantization process of communication signal input, infinitely approximating the unknown nonlinear term in the heading control system to eliminate the influence of implicit uncertainty factor on the control system. At the same time, the RBF adaptive quantization 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 execution frequency and diminish system control amplitude. The effectiveness of the designed motion control method is verified by constructing a simulation model in the environment of Matlab Simulink.
2024,46(15): 34-39 收稿日期：2023-10-07
DOI：10.3404/j.issn.1672-7649.2024.15.006
分类号：TP273
作者简介：郁榴华（1992 – ），男，硕士，工程师， 研究方向为仿真控制、船舶智能装备、船舶装配设计等
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