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Record W4381327614 · doi:10.1109/tpel.2023.3287828

A Review of Sliding Mode Observer Based Sensorless Control Methods for PMSM Drive

2023· review· en· W4381327614 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueIEEE Transactions on Power Electronics · 2023
Typereview
Languageen
FieldEngineering
TopicSensorless Control of Electric Motors
Canadian institutionsConcordia University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsRobustness (evolution)Control theory (sociology)Control engineeringComputer sciencePermanent magnet synchronous motorObserver (physics)Control (management)Sliding mode controlRotor (electric)EngineeringArtificial intelligencePhysicsNonlinear system

Abstract

fetched live from OpenAlex

For permanent magnet synchronous machines (PMSMs), high-performance control strategies rely on sensors to obtain accurate information of rotor position and speed. However, mechanical sensors are expensive and susceptible to harsh environment. Therefore, various sensorless control strategies have been proposed and intensively investigated for decades. Among them, sliding mode observer (SMO) based sensorless control method has drawn increasing attention due to its simple implementation and strong robustness. This article presents a comprehensive review of SMO-based sensorless control strategies for PMSMs reported in the literature. State-of-the-art SMO-based sensorless control strategies have been reviewed and investigated, and the design of SMO under nonideal conditions is presented as well. In addition, future research trends for SMO-based sensorless control strategies are also discussed.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.849
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.002
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.048
GPT teacher head0.370
Teacher spread0.322 · 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