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Record W3202639920 · doi:10.1109/tpel.2021.3116096

Discrete-Time Super-Twisting Sliding Mode Current Controller With Fixed Switching Frequency for Switched Reluctance Motors

2021· article· en· W3202639920 on OpenAlexafffund
Filipe Pinarello Scalcon, Gaoliang Fang, Rodrigo Padilha Vieira, H.A. Gründling, Ali Emadi

Bibliographic record

VenueIEEE Transactions on Power Electronics · 2021
Typearticle
Languageen
FieldEngineering
TopicElectric Motor Design and Analysis
Canadian institutionsMcMaster University
FundersCanadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of CanadaCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsControl theory (sociology)Switched reluctance motorBenchmark (surveying)Controller (irrigation)Digital controlComputer scienceHysteresisDiscrete time and continuous timeTracking (education)Stability (learning theory)Mode (computer interface)Control engineeringEngineeringControl (management)Electronic engineeringMathematicsRotor (electric)Physics

Abstract

fetched live from OpenAlex

This article from a discrete-time model and stability analysis is developed using a Lyapunov approach, taking into account the one sample implementation delay present in digital control applications. Experimental results are provided to demonstrate the effectiveness of the proposed approach, where a conventional hysteresis controller is used as a benchmark. Results show that the proposal is able to deliver adequate reference tracking while using significantly lower sampling frequencies. Moreover, when compared to the hysteresis controller, superior tracking is observed at low speeds, while maintaining a similar level of computational complexity.

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)0.000
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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 science0.0010.000
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 head0.008
GPT teacher head0.226
Teacher spread0.218 · 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

Citations47
Published2021
Admission routes2
Has abstractyes

Explore more

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