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Record W3006145215 · doi:10.1109/tie.2020.2972433

Discrete-Time SMO Sensorless Control of Current Source Converter-Fed PMSM Drives With Low Switching Frequency

2020· article· en· W3006145215 on OpenAlexafffund
Li Ding, Yunwei Li, Navid R. Zargari

Bibliographic record

VenueIEEE Transactions on Industrial Electronics · 2020
Typearticle
Languageen
FieldEngineering
TopicSensorless Control of Electric Motors
Canadian institutionsRockwell Automation (Canada)University of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsControl theory (sociology)Robustness (evolution)InverterObserver (physics)Computer scienceCapacitorLC circuitRotor (electric)Controller (irrigation)Low-pass filterFilter (signal processing)EngineeringVoltageControl (management)Physics

Abstract

fetched live from OpenAlex

In this article, a sensorless control method for medium- and high-speed operation is proposed for a current source converter (CSC)-fed permanent magnet synchronous machine (PMSM) with low switching frequency. The low switching frequency as well as inverter-side capacitor filter can cause great challenges in controller and rotor speed/position observer design, especially under higher speed operation due to the limited updates per fundamental cycle and approaching to the LC resonant point. To improve the system dynamic performance and attenuate the LC resonant, a multi-loop controller with capacitor voltage control was added into conventional field-oriented control. Moreover, an exact discrete-time sliding mode observer based sensorless strategy with adaptive filter is proposed to improve the rotor speed and position estimation accuracy as well as robustness to system uncertainty. The effectiveness of the proposed method is verified on a transformerless CSC-fed PMSM drives with both simulation and experiment.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.013
GPT teacher head0.207
Teacher spread0.194 · 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

Citations109
Published2020
Admission routes2
Has abstractyes

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Same venueIEEE Transactions on Industrial ElectronicsSame topicSensorless Control of Electric MotorsFrench-language works237,207