基于时变RBF神经网络的时变非线性系统控制器设计  

Time-varying RBF Neural Network-based Controller Design for a Class of Time-varying Nonlinear Systems

作　　者：李静 张涛涛 金凯 袁胜智 查子龙 Li Jing;Zhang Taotao;Jin Kai;Yuan Shengzhi;Zha Zilong(College of Weapons Engineering,Naval University of Engineering,WuHan 430033,China)

机构地区：[1]海军工程大学兵器工程学院,湖北武汉430033

出　　处：《系统仿真学报》2023年第10期2223-2236,共14页Journal of System Simulation

摘　　要：提出了一种具有时变特性的RBF神经网络,并证明了其逼近定理。以一类非参数化不确定时变非线性系统为研究对象,在用时变RBF神经网络对系统中的时变不确定性进行逼近的同时,综合应用Lyapunov稳定性理论和自适应迭代学习控制技术,设计了控制系统,并得到了稳定性定理,解决了这类系统的控制问题。仿真结果验证了时变神经网络的有效性和控制器设计方案的正确性。A time-varying RBF neural network with time-varying properties is firstly proposed,and its approximation theorem is obtained.For a class of nonlinear systems with non-parametric time-varying uncertainties,the proposed time-varying RBF neural network is used to approximate the time-varying uncertainties,and the controller is designed by making use of Lyapunov stability theory and adaptive iterative learning control techniques.We obtain the stability theorem of the designed controller.The simulation results verify the effectiveness of the time-varying neural network and the correctness of the controller design scheme.

关 键 词：时变非线性 RBF神经网络 迭代学习 自适应控制 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]