Full-Speed-Range Sensorless Control of High-Speed Permanent Magnet Synchronous Motor Based on the Prediction Methodology

In this article, a high-speed permanent magnet synchronous motor (HSPMSM) full-speed-range sensorless control strategy is proposed based on the prediction methodology. By leveraging the predictive idea, on the one hand, this strategy generates an error current that encompasses position information, from which the speed and position are directly demodulated during the start stage to adjust the given current vector in real time. It effectively reduces motor speed fluctuations at startup, enhances system anti-interference, and seamlessly transitions from the start algorithm to the double closed-loop sensorless control algorithm. On the other hand, it establishes the relationship between the position estimation error and the predicted current and the measured current, aiming to enhance the position estimation precision and optimize the HSPMSM double closed-loop sensorless control performance. Importantly, this strategy does not need the additional voltage sensors for the instantaneous power and can obtain the total position estimation error without supplementary controller design, which is highly practical. The control performance of the proposed strategy is verified on a 1.1 kW, 30000 r/min HSPMSM experimental platform, and compared with the traditional method. The experimental results prove the feasibility and effectiveness of the proposed strategy.

Authors

Institutions

Publication Details

Journal
IEEE Transactions on Power Electronics
Published
2024-10-25
DOI
https://doi.org/10.1109/tpel.2024.3484524
Citations
4
Primary Topic
Sensorless Control of Electric Motors
Type
article
Field-Weighted Citation Impact
0.83
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Full-Speed-Range Sensorless Control of High-Speed Permanent Magnet Synchronous Motor Based on the Prediction Methodology

Yanfei Cao, Changliang Xia, Tingna Shi, Mengting Ye et al.
4 citations
IEEE Transactions on Power Electronics
Sensorless Control of Electric Motors
0.83
article

Full-Speed-Range Sensorless Control of High-Speed Permanent Magnet Synchronous Motor Based on the Prediction Methodology

Yanfei Cao, Changliang Xia, Tingna Shi, Mengting Ye, Wei Chen
article en
4 citations

Abstract

In this article, a high-speed permanent magnet synchronous motor (HSPMSM) full-speed-range sensorless control strategy is proposed based on the prediction methodology. By leveraging the predictive idea, on the one hand, this strategy generates an error current that encompasses position information, from which the speed and position are directly demodulated during the start stage to adjust the given current vector in real time. It effectively reduces motor speed fluctuations at startup, enhances system anti-interference, and seamlessly transitions from the start algorithm to the double closed-loop sensorless control algorithm. On the other hand, it establishes the relationship between the position estimation error and the predicted current and the measured current, aiming to enhance the position estimation precision and optimize the HSPMSM double closed-loop sensorless control performance. Importantly, this strategy does not need the additional voltage sensors for the instantaneous power and can obtain the total position estimation error without supplementary controller design, which is highly practical. The control performance of the proposed strategy is verified on a 1.1 kW, 30000 r/min HSPMSM experimental platform, and compared with the traditional method. The experimental results prove the feasibility and effectiveness of the proposed strategy.

IEEE Transactions on Power ElectronicsVol. 40(2)
Tiangong University (CN), Zhejiang University (CN)
Affordable and clean energy
Openalex Percentile: Top 26%
Sensorless Control of Electric Motors
0.83
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.