Robust h∞ control for delayed nonlinear systems based on standard neural network models

Authors:
Mei-Qin Liu
Affiliations:
College of Electrical Engineering, Zhejiang University, Hangzhou, China
Venue:
ISNN'06 Proceedings of the Third international conference on Advnaces in Neural Networks - Volume Part II
Year:
2006

Citing 4
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Abstract

A neural-network-based robust output feedback H∞ control design is suggested for control of a class of nonlinear systems with time delays. The design approach employs a neural network, of which the activation functions satisfy the sector conditions, to approximate the delayed nonlinear system. A full-order dynamic output feedback controller is designed for the approximating neural network. The closed-loop neural control system is transformed into a novel neural network model termed standard neural network model (SNNM). Based on the robust H∞ performance analysis of the SNNM, the parameters of output feedback controllers can be obtained by solving some lilinear matrix inequalities (LMIs). The well-designed controller ensures the asymptotic stability of the closed-loop system and guarantees an optimal H∞ norm bound constraint on disturbance attenuation for all admissible uncertainties.