Adaptive neural control for a class of uncertain stochastic nonlinear systems with dead-zone

被引：23
|
作者：
Yu, Zhaoxu [1 ]
Du, Hongbin [1 ]
机构：
[1] E China Univ Sci & Technol, Dept Automat, Shanghai 200237, Peoples R China
来源：
JOURNAL OF SYSTEMS ENGINEERING AND ELECTRONICS | 2011年 / 22卷 / 03期
基金：
中国国家自然科学基金;
关键词：
adaptive control; neural network (NN); backstepping; stochastic nonlinear system; NETWORK CONTROL; STABILIZATION; TRACKING;
D O I：
10.3969/j.issn.1004-4132.2011.03.020
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
The problem of adaptive stabilization is addressed for a class of uncertain stochastic nonlinear strict-feedback systems with both unknown dead-zone and unknown gain functions. By using the backstepping method and neural network (NN) parameterization, a novel adaptive neural control scheme which contains fewer learning parameters is developed to solve the stabilization problem of such systems. Meanwhile, stability analysis is presented to guarantee that all the error variables are semi-globally uniformly ultimately bounded with desired probability in a compact set. The effectiveness of the proposed design is illustrated by simulation results.
引用
收藏
页码：500 / 506
页数：7
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