文献类型：会议论文

英文题名：Underactuated ship way-points track control using repetitive learning neurofuzzy

作者：Zhuo, Yongqiang[1]; Guo, Chen[2]

机构：[1] Navigation College, Guangdong Ocean University, Zhanjiang 524025, China; [2] Marine Dynamic Simulation and Control Lab, Dalian Maritime University, Dalian 116026, China

会议论文集：2013 25th Chinese Control and Decision Conference, CCDC 2013

会议日期：May 25, 2013 - May 27, 2013

会议地点：Guiyang, China

语种：英文

外文关键词：Backpropagation - Ships - Navigation - Fuzzy inference

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. ? 2013 IEEE.