Observer-based adaptive neural control for a class of nonlinear pure-feedback systems

被引：28

作者：

Wang, Honghong ^{[1
]}

Chen, Bing ^{[1
]}

Lin, Chong ^{[1
]}

Sun, Yumei ^{[1
]}

机构：

[1] Qingdao Univ, Inst Complex Sci, Qingdao 266071, Shandong, Peoples R China

来源：

NEUROCOMPUTING | 2016年 / 171卷

基金：

中国国家自然科学基金;

关键词：

Adaptive control; Observer; Neural networks; Backstepping; Nonlinear pure-feedback systems; DYNAMIC SURFACE CONTROL; LARGE-SCALE SYSTEMS; TIME-DELAY SYSTEMS; TRACKING CONTROL; NETWORK CONTROL; STABILIZATION;

D O I：

10.1016/j.neucom.2015.07.103

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper focuses on the issue of adaptive neural output-feedback control for a class of nonlinear pure-feedback systems. A state observer is first constructed to estimate the unmeasurable state variables. Radial basis function (RBF) neural networks (NNs) are used to approximate the unknown nonlinear functions and backstepping adaptive technique is utilized to construct controller. The proposed control scheme guarantees semi-globally uniformly ultimate boundedness (SGUUB) of all the signals in the closed-loop system. At last, a simulation example is used to show the effectiveness of the proposed method. (C) 2015 Elsevier B.V. All rights reserved.

引用

页码：1517 / 1523

页数：7

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