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Journal of Zhejiang University SCIENCE A 2008 Vol.9 No.8 P.1015~1023

http://doi.org/10.1631/jzus.A0720122


A new neural network model for the feedback stabilization of nonlinear systems


Author(s):  Mei-qin LIU, Sen-lin ZHANG, Gang-feng YAN

Affiliation(s):  School of Electrical Engineering, Zhejiang University, Hangzhou 310027, China

Corresponding email(s):   liumeiqin@zju.edu.cn

Key Words:  Standard neural network model (SNNM), Linear matrix inequality (LMI), Nonlinear control, Asymptotic stability, Chaotic cellular neural network, Takagi and Sugeno (T-S) fuzzy model


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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.

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T1 - A new neural network model for the feedback stabilization of nonlinear systems
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EP - 1023
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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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