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Record W2979526210 · doi:10.1049/iet-epa.2019.0033

Multivariable sliding‐mode extremum seeking PI tuning for current control of a PMSM

2019· article· en· W2979526210 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIET Electric Power Applications · 2019
Typearticle
Languageen
FieldEngineering
TopicExtremum Seeking Control Systems
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsControl theory (sociology)Multivariable calculusPiPID controllerMode (computer interface)Sliding mode controlControl engineeringControl (management)Computer scienceEngineeringPhysicsMathematicsNonlinear systemTemperature controlArtificial intelligence

Abstract

fetched live from OpenAlex

High‐performance current control is critical for obtaining smooth output torque in permanent‐magnet synchronous motors (PMSMs). To this end, a new proportional–integral (PI) tuning method based on multivariable sliding‐mode extremum seeking is proposed in this study and applied for current control of a PMSM. In the proposed method, a sliding‐mode extremum seeking optimiser varies the PI gains by minimising a cost function based on the feedback error term. The resulting PI controller can achieve fast and accurate tracking response, high disturbance rejection, and low sensitivity to PMSM parameter variations. The stability of the proposed control strategy is investigated through a Lyapunov analysis and its performance is evaluated through experimental studies. The results indicate that the proposed controller can offer improved performance in terms of accuracy, parametric variations, and load torque disturbances when compared with a conventional PI and a recently proposed PI controller using the gradient‐based extremum seeking tuning method.

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.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.995
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.236
Teacher spread0.229 · 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