基于逆推算法的无人艇神经网络滑模控制  

Backstepping Based on USV Sliding Model Control With Neural Network

作　　者：王仁强[1] 李敬东 邓华[1] 缪克银[1] 赵越[1] 杜加宝[1] WANG Ren-qiang;LI Jin-dong;DENG Hua;MIAO Ke-yin;ZHAO Yue;DU Jia-bao(School of Navigation,Jiangsu Maritime Institute,Nanjing Jiangsu 211700,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2期40-43,共4页Journal of Guangzhou Maritime University

基　　金：江苏海事职业技术学院千帆项目(XR1501);江苏高校品牌专业建设工程资助项目(PPZY2015B177)

摘　　要：设计一种基于逆推算法的无人艇神经网络滑模控制器,实现了无人艇在大幅度改向操纵运动中航向准确稳定快速跟踪.借助滑模变结构控制技术,设计系统带有积分器的滑模面,引入径向基神经网络逼近系统非线性函数和不确定参数,同时结合非线性阻尼定律克服外界有界干扰,最后利用逆推算法设计出系统控制律.仿真实验结果表明,径向基神经网络能精确逼近船舶非线性函数和不确定参数,控制器输出平滑无抖震,航向输出对船舶参数摄动及外界干扰不敏感,具有较强的鲁棒性.A type of neural network sliding mode control for USV was investigated based on backstepping algorithm,which realizes the accurate and stable tracking of the USV steering process,on the basis of backstepping method. First of all,an integrator sliding surface were designed with the sliding mode variable structure control technology. Secondly,radial basis function neural network was applied to approximate the system nonlinear function and uncertain parameters. Furthermore,a nonlinear damping law was introduced to overcome the bounded outside interference. Finally,on the basis of the above,the system control law was deduced by using the backstepping method. The simulation results show that the neural network can accurately approximate the nonlinear function and uncertain parameters of the ship,and the controller output is smooth and the heading output is not sensitive to perturbation of parameters and external disturbance,and the controller has strong robustness.

关 键 词：无人艇 逆推算法 神经网络 滑模控制 

分 类 号：U666.153[交通运输工程—船舶及航道工程]