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Record W2168391155 · doi:10.1109/psec.2002.1023886

Model reference adaptive system pseudoreduced-order flux observer for very low speed and zero speed estimation in sensorless induction motor drives

2003· article· en· W2168391155 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.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSensorless Control of Electric Motors
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsMRASControl theory (sociology)Induction motorObserver (physics)Computer scienceAdaptive systemVector controlFlexibility (engineering)Control engineeringEngineeringMathematicsPhysicsVoltageArtificial intelligenceControl (management)

Abstract

fetched live from OpenAlex

Sensorless induction motor (IM) drives are widely used in industry for their reliability and flexibility, particularly in hostile environment. However, the performance of many of previously developed observer based speed sensors in very low speeds of IM drives was not satisfactory. The authors have proposed and developed a model reference adaptive system (MRAS) based sensorless induction motor drive, which has high performance in very low speeds. In this novel scheme, an adaptive pseudoreduced-order flux observer (APFO) is used instead of the adaptive full-order flux observer (AFFO). In comparison with the AFFO, this method consumes less computational time, and provides a better speed estimation at very low speeds. This paper presents the theory, modelling, simulation and experimental results of the proposed MRAS pseudoreduced-order flux observer based sensorless field-oriented induction motor drives.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.094
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 head0.031
GPT teacher head0.231
Teacher spread0.200 · 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

Quick stats

Citations26
Published2003
Admission routes1
Has abstractyes

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