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A PWM Fixed-Gain Super-Twisting Sliding Mode Current Controller for Switched Reluctance Motors

2022· article· en· W4310971538 on OpenAlexafffund
Filipe Pinarello Scalcon, Gaoliang Fang, César José Volpato Filho, H.A. Gründling, Rodrigo Padilha Vieira, Babak Nahid‐Mobarakeh

Bibliographic record

VenueIECON 2022 – 48th Annual Conference of the IEEE Industrial Electronics Society · 2022
Typearticle
Languageen
FieldEngineering
TopicElectric Motor Design and Analysis
Canadian institutionsMcMaster University
FundersMitacs
KeywordsSwitched reluctance motorControl theory (sociology)Reluctance motorPulse-width modulationController (irrigation)Current (fluid)Mode (computer interface)Computer scienceTorqueEngineeringPhysicsControl (management)VoltageElectrical engineeringArtificial intelligence

Abstract

fetched live from OpenAlex

Proper current control is essential in SRM drives in order to ensure adequate reference tracking and torque ripple reduction when using current profiling techniques. In this context, this paper proposes a PWM super-twisting sliding mode current controller for switched reluctance motors with a fixed-gain structure. The approach presents a model-free structure, not requiring model information on implementation. The PWM implementation ensures a fixed switching frequency and the fixed-gain approach leads to a simple control structure, not demanding any sort of gain lookup tables. The complex task of gain design for super-twisting controllers is solved in this paper by means of an optimization-based design methodology, using the sum of the squared error as a cost function. The proposed technique is compared to a higher sampling hysteresis controller in terms of current and torque root-mean-square error. Simulation results are presented, showing that the proposal achieves a performance comparable to a higher sampling hysteresis controller.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.693
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
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 head0.035
GPT teacher head0.248
Teacher spread0.213 · 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.

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

Citations1
Published2022
Admission routes2
Has abstractyes

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Same venueIECON 2022 – 48th Annual Conference of the IEEE Industrial Electronics SocietySame topicElectric Motor Design and AnalysisFrench-language works237,207