Back-EMF Based Self-Sensing Drive for Ultra-High-Speed Surface Mount PMSM

This paper presents an improved rotor position estimation algorithm for ultra-high-speed surface mount permanent magnet synchronous machines (SPMSM). The proposed approach combines a synchronous frame back-EMF state filter and a motion observer structure to accurately estimate rotor position at speeds above 60 kRPM, where conventional position sensing techniques struggle. The state filter and motion observer are designed directly in the discrete domain considering necessary latch dynamics and computational delays. Simulation results for a 2-pole SPMSM show stable performance and close to zero phase lag position estimation at a fundamental frequency of 2.6 kHz. The implementation ensures stable control performance at sampling to fundamental frequency ratios as low as 4. A detailed experimental validation of the proposed algorithm on a high speed bearingless machine has been presented. Closed loop operation at 60 kRPM with a sampling frequency of 4 kHz, along with stable levitation of the bearingless machine was achieved. The proposed methodology is a significant advancement toward precise and reliable rotary motion state estimation for ultra-high-speed SPMSMs.

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Publication Details

Journal
IEEE Transactions on Industry Applications
Published
2025-05-19
DOI
https://doi.org/10.1109/tia.2025.3571362
Citations
1
Primary Topic
Iterative Learning Control Systems
Type
article
Field-Weighted Citation Impact
1.21
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article

Back-EMF Based Self-Sensing Drive for Ultra-High-Speed Surface Mount PMSM

Eric L. Severson, Nathan Petersen, Anirudh Upadhyaya, Aravind M. Nair
1 citations
IEEE Transactions on Industry Applications
Iterative Learning Control Systems
1.21
article

Back-EMF Based Self-Sensing Drive for Ultra-High-Speed Surface Mount PMSM

Eric L. Severson, Nathan Petersen, Anirudh Upadhyaya, Aravind M. Nair
article en
1 citations

Abstract

This paper presents an improved rotor position estimation algorithm for ultra-high-speed surface mount permanent magnet synchronous machines (SPMSM). The proposed approach combines a synchronous frame back-EMF state filter and a motion observer structure to accurately estimate rotor position at speeds above 60 kRPM, where conventional position sensing techniques struggle. The state filter and motion observer are designed directly in the discrete domain considering necessary latch dynamics and computational delays. Simulation results for a 2-pole SPMSM show stable performance and close to zero phase lag position estimation at a fundamental frequency of 2.6 kHz. The implementation ensures stable control performance at sampling to fundamental frequency ratios as low as 4. A detailed experimental validation of the proposed algorithm on a high speed bearingless machine has been presented. Closed loop operation at 60 kRPM with a sampling frequency of 4 kHz, along with stable levitation of the bearingless machine was achieved. The proposed methodology is a significant advancement toward precise and reliable rotary motion state estimation for ultra-high-speed SPMSMs.

IEEE Transactions on Industry ApplicationsVol. 61(6)
University of Minnesota (US), University of Wisconsin–Madison (US)
Affordable and clean energy
Openalex Percentile: Top 21%
Iterative Learning Control Systems
1.21
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