Smith Predictor-Based Decoupled Discrete Current Control for Five-phase PMSM at Low Sampling-to-Fundamental Frequency Ratios

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

Abstract

With the increase of high-reliability requirements, the multiphase machine draws considerable attention from the field of transportation electrification. However, the presence of third-order harmonic subspaces in five-phase machines poses greater challenges for high-speed applications, particularly due to the low sampling-to-fundamental (S2F) frequency ratios observed in higher-order subspaces. In this case, the electrical speed in third-order harmonic subspace is three times of that in fundamental subspace. The closed-loop control system performance deteriorates gradually or even becomes unstable due to cross-coupling in dq axis. Therefore, a Smith predictor-based decoupling control is proposed to eliminate the cross-coupling at low S2F frequency ratios. Additionally, the proposed method offers a solution to ensure the control stability of high-speed five-phase motors.

Original languageEnglish
Title of host publicationPEAS 2023 - 2023 IEEE 2nd International Power Electronics and Application Symposium, Conference Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages518-522
Number of pages5
ISBN (Electronic)9798350313765
DOIs
Publication statusPublished - 2023
Event2nd IEEE International Power Electronics and Application Symposium, PEAS 2023 - Guangzhou, China
Duration: 10 Nov 202313 Nov 2023

Publication series

NamePEAS 2023 - 2023 IEEE 2nd International Power Electronics and Application Symposium, Conference Proceedings

Conference

Conference2nd IEEE International Power Electronics and Application Symposium, PEAS 2023
Country/TerritoryChina
CityGuangzhou
Period10/11/2313/11/23

Keywords

  • Five-phase PMSM
  • sampling-to-fundamental (S2F)
  • Smith predictor

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering
  • Control and Optimization

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