Neural network based robust adaptive control for a class of nonlinear systems

Authors:
Dan Wang;Jin Wang
Affiliations:
School of Automation and Electrical Engineering, Dalian Maritime University, Dalian, P.R. China;Dept. of Chemical Engineering, West Virginia University Institute of Technology, Montgomery, WV
Venue:
ISNN'06 Proceedings of the Third international conference on Advnaces in Neural Networks - Volume Part II
Year:
2006

Citing 8
Cited 0

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Brief A combined backstepping and small-gain approach to adaptive output feedback control

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Abstract

A neural network based robust adaptive control design scheme is developed for a class of nonlinear systems represented by input-output models with an unknown nonlinear function and unmodeled dynamics. By on-line approximating the unknown nonlinear functions and unmodeled dynamics by radial basis function (RBF) networks, the proposed approach does not require the unknown parameters to satisfy the linear dependence condition. It is proved that with the proposed control law, the closed-loop system is stable and the tracking error converges to zero in the presence of unmodeled dynamics and unknown nonlinearity.