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Record W2137176517 · doi:10.1109/iemdc.2003.1210365

An improved control method of buried-type IPM bearingless motors considering magnetic saturation and magnetic pull variation

2003· article· en· W2137176517 on OpenAlexaff
M. Ooshima, Koji Yamashita, Akira Chibá, M.A. Rahman, T. Fukao

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMagnetic Bearings and Levitation Dynamics
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsControl theory (sociology)Magnetic bearingArmature (electrical engineering)StatorMagnetDecoupling (probability)TorqueMagnetic fluxFlux linkageElectromagnetic coilMaterials scienceDirect torque controlPhysicsMagnetic fieldEngineeringInduction motorComputer scienceControl engineeringMechanical engineeringElectrical engineeringVoltage

Abstract

fetched live from OpenAlex

An improved control method of the buried-type interior permanent magnet (IPM) bearingless motors has been presented. It is shown that the conventional method is not applicable to this type of IPM bearingless motors for loaded conditions. In IPM bearingless motors, the armature reaction flux is present due to high magnetic permeance with thin permanent magnets and small airgap length. An increase in d-axis flux linkage is caused by armature reaction as a torque component flux is increased. Thus, it is likely that magnetic saturation occurs in the stator teeth. In addition, a magnetic attractive force caused by the displacement force factor is dependent on the armature reaction flux. A new decoupling controller for the IPM bearingless motor considering magnetic saturation is proposed in this paper. It also considers the influence of magnetic attractive force variations. In addition, a new parameter identification method for the decoupling controller is also proposed. The new controller is found quite suitable to realize successful stable operation of the experimental IPM bearingless motor.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.930
Threshold uncertainty score0.726

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 head0.213
Teacher spread0.207 · 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

Citations10
Published2003
Admission routes1
Has abstractyes

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