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

This source preferred by Hammadi Nait-Charif

Authors: Zouggar, S., Nait-Charif, H. and Azizi, M.

http://www.edpsciences.org/10.1051/epjap:2000142

Journal: The European Physical Journal - Applied Physics

Volume: 11

Pages: 21-27

ISSN: 1286-0042

DOI: 10.1051/epjap:2000142

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.

This data was imported from Scopus:

Authors: Zouggar, S., Nait Charif, H. and Azizi, M.

Journal: EPJ Applied Physics

Volume: 11

Issue: 1

Pages: 21-27

ISSN: 1286-0042

DOI: 10.1051/epjap:2000142

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.

The data on this page was last updated at 05:24 on October 27, 2020.