Adaptive nonlinear control using input normalized neural networks

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An adaptive feedback linearization technique combined with the neural network is addressed to control uncertain nonlinear systems. The neural network-based adaptive control theory has been widely studied. However, the stability analysis of the closed-loop system with the neural network is rather complicated and difficult to understand, and sometimes unnecessary assumptions are involved. As a result, unnecessary assumptions for stability analysis are avoided by using the neural network with input normalization technique. The ultimate boundedness of the tracking error is simply proved by the Lyapunov stability theory. A new simple update law as an adaptive nonlinear control is derived by the simplification of the input normalized neural network assuming the variation of the uncertain term is sufficiently small.
Publisher
Springer Verlag (Germany)
Issue Date
2008-06
Keywords

Adaptive nonlinear control; Neural networks; Input normalization; Feedback Linearization; Uncertain systems

Citation

Journal of Mechanical Science and Technology, Vol.22, No.6, pp.1073-1083

ISSN
1738-494X
DOI
10.1007/s12206-007-1119-1
URI
http://hdl.handle.net/10203/18625
Appears in Collection
AE-Journal Papers(저널논문)
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[이현재][Adaptive Nonlinar Control Using Input Normalized Neural Networks][JMST][2008].pdf(498.07 kB)Download

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