文献类型：会议论文

中文题名：Underactuated Ship Way-points Track Control using Repetitive Learning Neurofuzzy

作者：Yongqiang Zhuo;Chen Guo

机构：[1]Navigation College, Guangdong Ocean University;[2]Marine Dynamic Simulation and Control Lab,Dalian Maritime University;

会议论文集：第25届中国控制与决策会议论文集

会议日期：20130525

会议地点：中国贵州贵阳

主办单位：东北大学;IEEE新加坡工业电子分会;IEEE控制系统协会哈尔滨分会

语种：英文

中文关键词：Underactuated Ship;Tracking Control;Repetitive Learning Control;Neurofuzzy

中文摘要：In order to dear with the large inertia and slow responsiveness to rudder changes of the underactuated ship, an on-line trained repetitive learning control scheme, which can be used to control a class of nonlinear ship movement systems, is proposed for ship way-points tracking control. A neurofizzzy learning algorithm is developed for track-keeping and track-changing. The controller uses fuzzy inference system to mimick experienced human operator and the back-propagation gradient descent method to update the network parameters through ship running. The convergence of the new method was mathematically proved. The design is independent of ship mathematical mode and enables real time control of a underactuated ship under disturbances. Applications are demonstrated and the approaches is verified efficient.