Artificial Neural Network Based on a Predictive Current Control in a DC-DC Buck Converter

Jazmin Ramirez-Hernandez, Leobardo Hernandez-Gonzalez, Oswaldo Ulises Juarez-Sandoval, Jose Pablo Garcia-Fernandez, Marcos Yair Bote-Vazquez

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

Abstract

Predictive control is a modern control strategy used in power converters that include switching devices in its topologies; is simple to understand and easy to be implemented, however, if the converter has to many operating modes the procedure may demand high computational requirements for high switching frequencies. This paper presents the inclusion of an artificial neural network in the controller, the predictive controller is used during the training phase and once the neural network is fine-tuned it can operate without the predictive control algorithm, minimizing the computational cost. The algorithm is validated by simulation results in Matlab-Simulink in a current control for a Buck converter.

Original languageEnglish
Title of host publication2021 23rd IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665434270
DOIs
StatePublished - 2021
Event23rd IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2021 - Virtual, Ixtapa, Mexico
Duration: 10 Nov 202112 Nov 2021

Publication series

Name2021 23rd IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2021

Conference

Conference23rd IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2021
Country/TerritoryMexico
CityVirtual, Ixtapa
Period10/11/2112/11/21

Keywords

  • Artificial Neural Network
  • Buck Converter
  • Predictive Control
  • current control

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