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基于RBF神经网络补偿的动力定位PD控制
引用本文:卢林枫,徐海祥,余文曌,冯辉. 基于RBF神经网络补偿的动力定位PD控制[J]. 船舶力学, 2021, 25(6): 772-780. DOI: 10.3969/j.issn.1007-7294.2021.06.009
作者姓名:卢林枫  徐海祥  余文曌  冯辉
作者单位:武汉理工大学高性能船舶技术教育部重点实验室 武汉430063;武汉理工大学交通学院 武汉430063
摘    要:动力定位控制系统在实际工况中会受到外部风、浪、流等环境力的扰动,本文主要研究PD+RBF控制算法以提高动力定位系统的抗干扰性和稳定性.RBF神经网络用来补偿外界环境扰动,其权值自适应律通过Lyapunov稳定性证明得到,通过稳定性的分析能够保证整个系统全局渐进稳定.在一艘平台供应船的仿真结果对比中显示了所提出控制器的有效性.

关 键 词:动力定位系统  自适应律  RBF神经网络  Lyapunov稳定性理论

PD Control with RBF Neural Network Compensation for Dynamic Positioning System
LU Lin-feng,XU Hai-xiang,YU Wen-zhao,FENG Hui. PD Control with RBF Neural Network Compensation for Dynamic Positioning System[J]. Journal of Ship Mechanics, 2021, 25(6): 772-780. DOI: 10.3969/j.issn.1007-7294.2021.06.009
Authors:LU Lin-feng  XU Hai-xiang  YU Wen-zhao  FENG Hui
Abstract:Dynamic positioning control system is influenced inevitably by external environment distur-bances such as wind, wave and current in actual working conditions. This paper mainly studies the RBF neural network and PD control algorithm to improve the immunity and stability of a dynamic posi-tioning system. RBF neural network is used to compensate for the environment disturbances, whose adaptability and robustness can be enhanced. The adaptive algorithm of weights is obtained by Lyapu-nov function analysis. Simulation comparison results of a supply ship confirm the effectiveness of the proposed controller.
Keywords:dynamic position system  adaptive algorithm  RBF neural network  Lyapunov theory
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