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Simple adaptive control for SISO nonlinear system using neural networks for magnetic levitation plant

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4 Author(s)
Yasser, M. ; Graduate Sch. of Sci. & Technol., Chiba Univ., Japan ; Jiunshian Phuah ; Jianming Lu ; Yahagi, T.

This paper presents a method of simple adaptive control (SAC) for single-input single-output (SISO) nonlinear systems using neural networks applied for magnetic levitation plant. The control input is given by the sum of the output of the simple adaptive controller and the output of the neural network. The neural network is used to compensate the nonlinearity of plant dynamic of magnetic levitation plant that is not taken into consideration in the usual SAC. The role of the neural network is to construct a linearized model by minimizing the output error caused by nonlinearities in the control systems.

Published in:

Circuits and Systems, 2004. MWSCAS '04. The 2004 47th Midwest Symposium on  (Volume:3 )

Date of Conference:

25-28 July 2004