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Record W1489744873 · doi:10.1109/lescpe.2002.1020670

Recent progress in sensorless vector-controlled induction motor drives

2003· article· en· W1489744873 on OpenAlexafffund
Hossein Madadi Kojabadi, Liuchen Chang, R. Doriaswami

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSensorless Control of Electric Motors
Canadian institutionsUniversity of New Brunswick
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsTachometerVector controlInduction motorResolverInverterControl engineeringReliability (semiconductor)Noise (video)Computer scienceTransducerControl theory (sociology)Rotary encoderEncoderEngineeringDC motorControl (management)Electrical engineeringRotor (electric)PhysicsVoltagePower (physics)Artificial intelligence

Abstract

fetched live from OpenAlex

This paper reports the basic concept and recent progress in sensorless vector-controlled inverter fed induction tor (IM) drives. In field oriented control, a rotational transducer such as a tachometer, an encoder or a resolver, was often mounted on the IM shaft. However, a speed sensor cannot be mounted on a shaft in some cases such as motor drives in a hostile environment or high-speed motor drives. Further, these sensors suffer from low reliability, high cost and noise. Therefore, a sensorless speed drive is increasingly employed in modern industrial applications. This paper gives a short review of sensorless vector control of an induction motor drive.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0010.003
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.002

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 head0.009
GPT teacher head0.214
Teacher spread0.205 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations9
Published2003
Admission routes2
Has abstractyes

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