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Fuzzy Speed Control Method Based on Particle Swarm Optimization for Five-Phase PMSM

  • School of Electrical Engineering and Automation, Harbin Institute of Technology

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

Abstract

Five-phase permanent-magnet synchronous machine (FP-PMSM) attracts extensive interest due to their enhanced fault tolerance and power density. However, industrial applications also raise further requirements for dynamic response performance of FP-PMSM. To resolve this issue, a fuzzy speed control method based on particle swarm optimization for five-phase PMSM is proposed. The method integrates expertise-based PI adaptation, where fuzzy logic dynamically adjusts gains using real-time speed error and its derivative, with systematic particle swarm optimization (PSO) of fuzzy factors to overcome empirical tuning limitations in fuzzy factor tunning. Simulation results demonstrate that this PSO improved fuzzy-PI control strategy significantly improves transient performance with reduced overshoot and oscillation compared to conventional PID control, validating its effectiveness for high-performance PMSM drives.

Original languageEnglish
Title of host publicationProceedings of the 2025 IEEE Transportation Electrification Conference and Expo, Asia-Pacific, ITEC Asia-Pacific 2025
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798331559847
DOIs
StatePublished - 2025
Externally publishedYes
Event2025 IEEE Transportation Electrification Conference and Expo, Asia-Pacific, ITEC Asia-Pacific 2025 - Singapore, Singapore
Duration: 25 Nov 202528 Nov 2025

Publication series

NameProceedings of the 2025 IEEE Transportation Electrification Conference and Expo, Asia-Pacific, ITEC Asia-Pacific 2025

Conference

Conference2025 IEEE Transportation Electrification Conference and Expo, Asia-Pacific, ITEC Asia-Pacific 2025
Country/TerritorySingapore
CitySingapore
Period25/11/2528/11/25

Keywords

  • PSOalgorithm
  • five-phase PMSM
  • fuzzy PI
  • self-tunning

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