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Record W1818307800 · doi:10.1109/iecon.1999.816536

Real time implementation of a rotor time-constant online estimation scheme

2003· article· en· W1818307800 on OpenAlexaff
A. Ba-Razzouk, A. Chériti, V. Rajagopalan

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSensorless Control of Electric Motors
Canadian institutionsUniversité du Québec à Trois-RivièresNatural Sciences and Engineering Research Council of Canada
Fundersnot available
KeywordsMATLABTorqueConstant (computer programming)Rotor (electric)Control theory (sociology)Scheme (mathematics)Computer scienceCoupling (piping)VoltageField (mathematics)SIGNAL (programming language)Estimation theoryFlux (metallurgy)Variation (astronomy)Time constantControl engineeringEngineeringControl (management)Electrical engineeringPhysicsAlgorithmMathematics

Abstract

fetched live from OpenAlex

Implementation of AC drives insensitive to parameter variation is a challenge which interests many researchers in the field of high performance drives. In the case of drives controlled by the indirect rotor flux orientated control method (IRFOC), the rotor time-constant (/spl tau//sub r/=R/sub r//L/sub r/) exerts a dominant role in the loss of dynamic performances and its variation results in creation of an undesirable coupling between flux and torque of the machine. This paper presents a new scheme for online estimation of this parameter using measurements of motor accessible variables (voltages, currents and speed). This scheme is tested by simulation in the MATLAB/SIMULINK environment and also real-time implemented on a TMS320C31 digital signal processor. Both simulation and experimental results are presented.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.006
GPT teacher head0.242
Teacher spread0.236 · 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 designSimulation or modeling
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

Citations10
Published2003
Admission routes1
Has abstractyes

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