A look-up table-based model predictive torque control of switched reluctance motor drives with improved prediction

Diego F. Valencia, Rasul Tarvirdilu-Asl, Cristian Garcia, Jose Rodriguez, Ali Emadi

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

Abstract

This paper proposes a predictive torque control strategy based on look-up tables with improved delay compensation to enhance prediction capabilities. The tables or static maps contain the flux linkage and torque characteristics of the machine, which provides higher prediction accuracy compared to approximated analytical models. The delay compensation is enhanced by including a Kalman filter stage. The algorithm is validated through simulations and experiments using a 5.5 kW, 12/8 SRM. The results evidenced improved torque sharing capabilities with respect to conventional methods and other predictive control strategies by offering a trade-off between torque ripple production, average torque and average torque per ampere.

Original languageEnglish
Title of host publication2021 IEEE Transportation Electrification Conference and Expo, ITEC 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages315-320
Number of pages6
ISBN (Electronic)9781728175836
DOIs
Publication statusPublished - 21 Jun 2021
Event2021 IEEE Transportation Electrification Conference and Expo, ITEC 2021 - Chicago, United States
Duration: 21 Jun 202125 Jun 2021

Publication series

Name2021 IEEE Transportation Electrification Conference and Expo, ITEC 2021

Conference

Conference2021 IEEE Transportation Electrification Conference and Expo, ITEC 2021
Country/TerritoryUnited States
CityChicago
Period21/06/2125/06/21

Keywords

  • Kalman filter
  • Model predictive control
  • Predictive torque control
  • Switched reluctance motor drive

ASJC Scopus subject areas

  • Automotive Engineering
  • Electrical and Electronic Engineering
  • Control and Optimization
  • Transportation

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