不确定条件下基于神经网络的船舶航向滑模鲁棒控制  

Robust Sliding Mode Control of Ship Based on Neural Network under Uncertain Conditions

作　　者：赵越[1] 王仁强[1] 刘昌华[1] 李敬东 孙建明[1] 陈大伟 ZHAO Yue;WANG Ren-qiang;LIU Chang-hua;LI Jing-dong;SUN Jian-ming;CHEN Da-wei(College of Navigation,Jiangsu Maritime Institute,Nanjing Jiangsu 211170,China)

机构地区：[1]江苏海事职业技术学院航海技术学院,江苏南京211170

出　　处：《广州航海学院学报》2018年第4期36-39,共4页Journal of Guangzhou Maritime University

基　　金：江苏海事职业技术学院课题(2017JGYB-03);江苏省高等学校自然科学研究面上项目(18KJB580003);江苏省高等学校大学生创新创业训练项目(201812679021H)

摘　　要：设计了一种不确定条件下基于神经网络的船舶航向滑模鲁棒控制算法.该算法能够有效决解模型不确定及外界扰动情况下的船舶运动控制问题.从船舶的非线性响应型运动数学模型出发,采用RBF神经网络对船舶系统函数及外部扰动进行有效逼近,再利用Lyapunov稳定性理论和Backstepping方法设计船舶航向控制器.仿真结果表明该控制算法能够很好地跟踪设定信号,并具有很好的鲁棒性.A robust control algorithm for ship heading sliding mode based on neural network under uncertain conditions is designed.The algorithm can effectively solve the problem of ship motion control under model uncertainty and external disturbance.Based on the nonlinear response motion mathematical model of the ship,the RBF neural network is used to effectively approximate the ship system function and external disturbance.Then the Lyapunov stability theory and Backstepping method are used to design the ship heading controller.The simulation results show that the control algorithm can track the set signal well and has good robustness.

关 键 词：船舶 控制 不确定 RBF神经网络 鲁棒 LYAPUNOV 

分 类 号：U666.153[交通运输工程—船舶及航道工程]