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Record W2147726464 · doi:10.1109/ias.1999.801630

Performance of current controllers for VSI-fed IPMSM drive

2003· article· en· W2147726464 on OpenAlexaff
M. Nasir Uddin, T.S. Radwan, Glyn George, M.A. Rahman

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicElectric Motor Design and Analysis
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsControl theory (sociology)ComparatorComputer scienceController (irrigation)Digital signal processingHarmonicInverterVoltageDigital signal processorFast Fourier transformHarmonic analysisElectronic engineeringEngineeringElectrical engineeringControl (management)Physics

Abstract

fetched live from OpenAlex

The current-controlled voltage source inverter (VSI)-fed interior permanent magnet synchronous motor (IPMSM) has received widespread interest in high precision industrial drive applications. This paper presents a detailed comparison of various current controller schemes, particularly hysteresis and ramp comparator controllers for the IPMSM drive based on performances at different speeds. The hysteresis and the ramp comparator controllers are getting more attention due to their simplicity and high dynamic responses. In this work, fixed, sinusoidal and mixed band hysteresis current controllers, as well as conventional ramp and improved ramp comparator controllers are considered. The harmonic spectra of the motor line currents for various current controllers are obtained using a fast Fourier transform (FFT) for comparison purposes. The comparison is based on experimental results. In order to compare the performances of various controllers, the complete drive system is implemented in real time using the digital signal processor (DSP) controller board DS-1102.

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)0.003
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: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.011
GPT teacher head0.215
Teacher spread0.204 · 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

Citations61
Published2003
Admission routes1
Has abstractyes

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