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Record W4364861964 · doi:10.23977/jeis.2023.080104

A Comparison Research on Sliding Mode Observation Methods for SPMSM in Sensorless Environment of Medium-to-High Speed

2023· article· en· W4364861964 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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJournal of Electronics and Information Science · 2023
Typearticle
Languageen
FieldEngineering
TopicSensorless Control of Electric Motors
Canadian institutionsnot available
Fundersnot available
KeywordsMRASControl theory (sociology)Observer (physics)Extended Kalman filterComputer scienceRotor (electric)Kalman filterPosition (finance)Mode (computer interface)Control engineeringControl (management)EngineeringVector controlInduction motorArtificial intelligencePhysics

Abstract

fetched live from OpenAlex

Aiming at sensorless control system of surface attached permanent magnet synchronous motor (SPMSP), three observation algorithms proposed in recent years to estimate rotor position and speed under medium-high speed operation were introduced in this paper. The working principles, advantages and disadvantages of these algorithms such as the Extended Kalman filter algorithm (EKF), Model Reference Adaptive System (MRAS) and Sliding Mode Observer (SMO) were compared, and the applicability of these three algorithms on SPMSP at present was compared and summarized. Furthermore, a sensorless control strategy based on improved SMO was designed to ensure sensorless control effect under medium-high speed operation of SPMSM.

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)0.005
metaresearch head score (Gemma)0.001
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: Empirical
Teacher disagreement score0.151
Threshold uncertainty score0.286

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.096
GPT teacher head0.424
Teacher spread0.328 · 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