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Journal of Zhejiang University SCIENCE A 2006 Vol.7 No.4 P.530~538

10.1631/jzus.2006.A0530


Interval standard neural network models for nonlinear systems


Author(s):  Liu Mei-qin

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

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

Key Words:  Interval standard neural network model (ISNNM), Linear matrix inequality (LMI), Nonlinear system, Asymptotic stability, Robust control


Liu Mei-qin. Interval standard neural network models for nonlinear systems[J]. Journal of Zhejiang University Science A, 2006, 7(4): 530~538.

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author="Liu Mei-qin",
journal="Journal of Zhejiang University Science A",
volume="7",
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publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.2006.A0530"
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T1 - Interval standard neural network models for nonlinear systems
A1 - Liu Mei-qin
J0 - Journal of Zhejiang University Science A
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PB - Zhejiang University Press & Springer
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DOI - 10.1631/jzus.2006.A0530


Abstract: 
A neural-network-based robust control design is suggested for control of a class of nonlinear systems. The design approach employs a neural network, whose activation functions satisfy the sector conditions, to approximate the nonlinear system. To improve the approximation performance and to account for the parameter perturbations during operation, a novel neural network model termed standard neural network model (SNNM) is proposed. If the uncertainty is bounded, the SNNM is called an interval SNNM (ISNNM). A state-feedback control law is designed for the nonlinear system modelled by an ISNNM such that the closed-loop system is globally, robustly, and asymptotically stable. The control design equations are shown to be a set of linear matrix inequalities (LMIs) that can be easily solved by available convex optimization algorithms. An example is given to illustrate the control design procedure, and the performance of the proposed approach is compared with that of a related method reported in literature.

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Reference

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