CLC number: TP273
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 0000-00-00
Cited: 3
Clicked: 6911
Mei-qin LIU, Sen-lin ZHANG, Gang-feng YAN. A new neural network model for the feedback stabilization of nonlinear systems[J]. Journal of Zhejiang University Science A, 2008, 9(8): 1015-1023.
@article{title="A new neural network model for the feedback stabilization of nonlinear systems",
author="Mei-qin LIU, Sen-lin ZHANG, Gang-feng YAN",
journal="Journal of Zhejiang University Science A",
volume="9",
number="8",
pages="1015-1023",
year="2008",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.A0720122"
}
%0 Journal Article
%T A new neural network model for the feedback stabilization of nonlinear systems
%A Mei-qin LIU
%A Sen-lin ZHANG
%A Gang-feng YAN
%J Journal of Zhejiang University SCIENCE A
%V 9
%N 8
%P 1015-1023
%@ 1673-565X
%D 2008
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.A0720122
TY - JOUR
T1 - A new neural network model for the feedback stabilization of nonlinear systems
A1 - Mei-qin LIU
A1 - Sen-lin ZHANG
A1 - Gang-feng YAN
J0 - Journal of Zhejiang University Science A
VL - 9
IS - 8
SP - 1015
EP - 1023
%@ 1673-565X
Y1 - 2008
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.A0720122
Abstract: A new neural network model termed ‘standard neural network model’ (SNNM) is presented, and a state-feedback control law is then designed for the SNNM to stabilize the closed-loop system. The control design constraints are shown to be a set of linear matrix inequalities (LMIs), which can be easily solved by the MATLAB LMI Control Toolbox to determine the control law. Most recurrent neural networks (including the chaotic neural network) and nonlinear systems modeled by neural networks or takagi and Sugeno (T-S) fuzzy models can be transformed into the SNNMs to be stabilization controllers synthesized in the framework of a unified SNNM. Finally, three numerical examples are provided to illustrate the design developed in this paper.
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