文献类型：会议论文

英文题名：Underactuated Ship Way-Points Tract Control Using Repetitive Learning Neurofuzzy

作者：Zhuo, Yongqiang[1];Guo, Chen[2]

机构：[1]Guangdong Ocean Univ, Nav Coll, Zhanjiang 524025, Peoples R China;[2]Dalian Maritime Univ, Marine Dynamic Simulat Control Lab, Dalian 116026, Peoples R China

会议论文集：25th Chinese Control and Decision Conference (CCDC)

会议日期：MAY 25-27, 2013

会议地点：Guiyang, PEOPLES R CHINA

语种：英文

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

外文摘要：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 neurofuzzy 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.