Neural Network-based Adaptive State-feedback Control for High-order Stochastic Nonlinear Systems  

作　　者：MIN Hui-Fang DUAN Na 

机构地区：[1]School of Electrical Engineering & Automation, Jiangsu Normal University

出　　处：《自动化学报》2014年第12期2968-2972,共5页Acta Automatica Sinica

基　　金：Supported by National Natural Science Foundation of China(61104222,61305149);Natural Science Foundation of JiangsuProvince(BK2011205);333 High-Level Talents Training Program inJiangsu Province;Program for Fundamental Research of Natural Sciences in Universities of Jiangsu Province(11KJB510026);Natural Science Foundation of Jiangsu Normal University(11XLR08)

摘　　要：This paper focuses on investigating the issue of adaptive state-feedback control based on neural networks(NNs)for a class of high-order stochastic uncertain systems with unknown nonlinearities. By introducing the radial basis function neural network(RBFNN) approximation method, utilizing the backstepping method and choosing an approximate Lyapunov function, we construct an adaptive state-feedback controller which assures the closed-loop system to be mean square semi-global-uniformly ultimately bounded(M-SGUUB). A simulation example is shown to illustrate the effectiveness of the design scheme.This paper focuses on investigating the issue of adaptive state-feedback control based on neural networks（NNs）for a class of high-order stochastic uncertain systems with unknown nonlinearities. By introducing the radial basis function neural network（RBFNN） approximation method, utilizing the backstepping method and choosing an approximate Lyapunov function, we construct an adaptive state-feedback controller which assures the closed-loop system to be mean square semi-global-uniformly ultimately bounded（M-SGUUB）. A simulation example is shown to illustrate the effectiveness of the design scheme.

关 键 词：径向基函数神经网络 状态反馈控制器 随机非线性系统 自适应 高阶 LYAPUNOV函数 非线性问题 不确定系统 

分 类 号：TP13[自动化与计算机技术—控制理论与控制工程]