文献类型：期刊文献

英文题名：Nonlinear sliding mode control for ships path following using neural-network approximation

作者：Xinyu, Chu[1]; Ronghui, Li[2]; Renxiang, Bu[1]; Zongxuan, Li[1]

机构：[1] Navigation College, Dalian Maritime University, Dalian, China; [2] Maritime College, Guangdong Ocean University, Zhanjiang, China

年份：2020

起止页码：298

外文期刊名：Proceedings - 2020 35th Youth Academic Annual Conference of Chinese Association of Automation, YAC 2020

收录：EI(收录号：20210809966913)

语种：英文

外文关键词：Ships - Radial basis function networks - Uncertainty analysis

外文摘要：This paper presents a nonlinear sliding mode control algorithm for the path following of the underactuated ships in presence of the uncertain parameters and unknown external disturbances. Firstly, the path following control is transformed into the heading control by the nonlinear sliding mode method, and the unknown lateral drift part is estimated by a nonlinear disturbance observer. Then, the heading control law is proposed by the nonlinear sliding mode and the radial basis function (RBF) neural networks, which could be employed to approximate the uncertain parameters or external disturbances. In the proposed algorithm, the ship could coverage to the desired path well and the unknown lateral drift part could be observed. Finally, the effectiveness and the robustness of the proposed algorithm have been verified through the numerical simulation. ? 2020 IEEE.