MétaCan
Menu
Back to cohort
Record W1986482919 · doi:10.1109/iecon.2012.6389303

Methodology and experimental set-up for DSP-based sensorless PWM speed estimation of induction machine

2012· article· en· W1986482919 on OpenAlexaff
Abdelrahaman Yousif Eshag Lesan, Mamadou Lamine Doumbia, Pierre Sicard

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSensorless Control of Electric Motors
Canadian institutionsUniversité du Québec à Trois-Rivières
Fundersnot available
KeywordsDigital signal processingVector controlInduction motorControl engineeringComputer sciencePower electronicsDigital signal processorElectronic speed controlPulse-width modulationDigital controlTorqueDigital signalMachine controlSIGNAL (programming language)Signal processingPower (physics)Electronic engineeringVoltageEngineeringComputer hardwareElectrical engineering

Abstract

fetched live from OpenAlex

Sensorless vector control is an instantaneous electromagnetic torque control technique applied to variable speed (AC) motor drives where the costly speed sensor is no longer used. It is characterized by its fast dynamic response and low cost. Sensorless variable speed drives incorporating induction machines immerged as a result of the progress in the field of power electronics and digital signal processing (DSP) technology. DSP substantially helped the implementation of complex control algorithms and the generation of patterns to control voltage source inverters. Despite the fact that the DSP technology is widely used in the industry and manufacturing data sheets are available, there are few comprehensive documents in the literature detailing the principles of development and implementation of the new generation of DSP. This paper presents the development of a DSP-based sensorless vector control of induction machine. The algorithm development methodology and the experimental results are presented for the TMS320F2812 processor.

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 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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.392
Threshold uncertainty score0.494

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 head0.062
GPT teacher head0.314
Teacher spread0.252 · 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 teacher head, 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

Citations1
Published2012
Admission routes1
Has abstractyes

Explore more

Same topicSensorless Control of Electric MotorsFrench-language works237,207