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Record W4402159312 · doi:10.1109/tte.2024.3446767

Enhanced Hybrid PWM for the Closed-Loop Control of Permanent Magnet Synchronous Motor Drives

2024· article· en· W4402159312 on OpenAlexaff
Battur Batkhishig, Pedro F. C. Gonçalves, Giorgio Pietrini, Babak Nahid‐Mobarakeh, Rohit Baranwal, Ali Emadi

Bibliographic record

VenueIEEE Transactions on Transportation Electrification · 2024
Typearticle
Languageen
FieldEngineering
TopicMultilevel Inverters and Converters
Canadian institutionsMcMaster University
Fundersnot available
KeywordsPulse-width modulationControl theory (sociology)Permanent magnet synchronous motorClosed loopSynchronous motorLoop (graph theory)AC motorMagnetComputer scienceControl (management)Control engineeringEngineeringElectric motorElectrical engineeringVoltageMathematicsArtificial intelligence

Abstract

fetched live from OpenAlex

Integrating asynchronous pulsewidth modulation (PWM) and synchronous optimal PWM (SOPWM) in a hybrid PWM scheme has proven to be an effective modulation strategy for ac motor drives operating over a wide speed range. However, hybrid PWM is rarely used in permanent magnet synchronous motor (PMSM) drives in traction applications due to the noise sensitivity issues associated with SOPWM and the complex PWM transition schemes. To overcome these drawbacks, this article introduces an enhanced hybrid PWM technique suitable for integration with the well-established field-oriented control (FOC) strategy. The proposed technique relies on an innovative robust SOPWM and simple smooth PWM transition schemes to improve the robustness of hybrid PWM against noise in the control loop introduced by the measured currents and rotor position. Experimental results are provided to validate the effectiveness of the proposed hybrid PWM technique used in the closed-loop control of a PMSM drive.

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: none
Teacher disagreement score0.961
Threshold uncertainty score0.615

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 head0.007
GPT teacher head0.213
Teacher spread0.205 · 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

Citations10
Published2024
Admission routes1
Has abstractyes

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