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Record W4408164413 · doi:10.1109/tpel.2025.3547247

Virtual Reduced-Order Model-Based Back EMF Estimation and Speed Sensorless Control for $LC$-Filtered PMSM Drives

2025· article· en· W4408164413 on OpenAlexaff
Cheng Xue, Xuesong Wu, Yuzhuo Li, Yunwei Li

Bibliographic record

VenueIEEE Transactions on Power Electronics · 2025
Typearticle
Languageen
FieldEngineering
TopicMagnetic Bearings and Levitation Dynamics
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsControl theory (sociology)Electronic speed controlControl engineeringComputer scienceVector controlMachine controlSynchronous motorOrder (exchange)Control (management)EngineeringVoltageElectrical engineeringInduction motor

Abstract

fetched live from OpenAlex

The installment of the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$LC$</tex-math></inline-formula> filter at the inverter output side reshapes the sinusoidal input voltage for the motor terminal, thus, extending a longer motor lifetime. Despite this, achieving speed sensorless control remains essential for enhanced reliability and saved costs. Currently, there is limited research on the development of a speed sensorless control tailored to high-order drive scenarios due to the increased complexity and strong coupling of the system modeling, and the unaltered adoption of the prevailing general observer methodology demands a considerably large dimensional gain matrix to guarantee observability. To fill this important research gap, this article proposes a novel back-electromotive force (EMF) modeling for the permanent magnet synchronous machine drives based on the weighted current between the filter inductor current and motor stator current. This new approach converts the third-order <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$LCL$</tex-math></inline-formula> model to the first-order <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$L$</tex-math></inline-formula> case (virtual reduced-order modeling), which enables the estimation of back EMF without relying on voltage sensors. In addition, the observer designed based on the proposed virtual model not only removes the dependency on capacitor parameters but also reduces the size of the gain matrix. This further enhances the robustness of the back EMF estimation and simultaneously simplifies the design and computation of the observation algorithm. A Kalman filter observer for back EMF estimation is implemented as a case study to verify the proposed modeling. The efficacy of the proposed speed sensorless control is also evaluated under scenarios involving variations in filter and motor inductance.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.892
Threshold uncertainty score0.987

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 head0.006
GPT teacher head0.226
Teacher spread0.221 · 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 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

Citations5
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueIEEE Transactions on Power ElectronicsSame topicMagnetic Bearings and Levitation DynamicsFrench-language works237,207