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Record W3153062593 · doi:10.1109/access.2021.3072360

Observers for High-Speed Sensorless PMSM Drives: Design Methods, Tuning Challenges and Future Trends

2021· article· en· W3153062593 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueIEEE Access · 2021
Typearticle
Languageen
FieldEngineering
TopicSensorless Control of Electric Motors
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of CanadaCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsComputer scienceControl theory (sociology)Control engineeringEngineeringControl (management)Artificial intelligence

Abstract

fetched live from OpenAlex

Rotor position and speed estimation methods are consolidated to reduce the cost and volume of permanent magnet synchronous motor (PMSM) sensorless control drives while maintaining high performance. Advanced nonlinear algorithms require accurate design for precise estimation tracking throughout the entire PMSM operation range. This paper presents a broad review of the main high-speed estimation methods for sensorless PMSM drives. First, the stability constraints and design methodologies of the main estimation techniques presented in the literature are discussed. In the second part, it is investigated the new observer design trends, which are used under non-ideal conditions, such as robustness to distortions, the effects of parameter variation, sensorless parameter estimation, and low sampling-frequency-to-speed ratio operation. Future trends on the design of observers for high-speed sensorless PMSM drives are also discussed.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.869
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.071
GPT teacher head0.315
Teacher spread0.244 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it