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Neural control and transient analysis of the LCL-type resonant converter

Published online by Cambridge University Press:  15 July 2000

S. Zouggar ,
H. Nait Charif  and
M. Azizi
S. Zouggar*
Affiliation:
University Mohammed 1er, École Supérieure de Technologie, Oujda Hay EL QODS, Complexe Universitaire, BP 473, 60000 Oujda, Morocco
H. Nait Charif
Affiliation:
Computer & Electrical Engineering department, College of Engineering Michigan State University, USA
M. Azizi
Affiliation:
University Mohammed 1er, École Supérieure de Technologie, Oujda Hay EL QODS, Complexe Universitaire, BP 473, 60000 Oujda, Morocco
*
zouggar@est.univ-oujda.ac.ma
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Abstract

This paper proposes a generalised inverse learning structure to control the LCL converter. A feedforward neural network is trained to act as an inverse model of the LCL converter then both are cascaded such that the composed system results in an identity mapping between desired response and the LCL output voltage. Using the large signal model, we analyse the transient output response of the controlled LCL converter in the case of large variation of the load. The simulation results show the efficiency of using neural networks to regulate the LCL converter.

Keywords

Type
Research Article
Information
The European Physical Journal - Applied Physics , Volume 11 , Issue 1 , July 2000 , pp. 21 - 27
Copyright
© EDP Sciences, 2000

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References

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