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Record W4396982275 · doi:10.1109/tmech.2023.3348139

Search-Algorithm-Based Offline Inductance Identification Using Sliding Mode Flux Observation Data for IPMSMS

2024· article· en· W4396982275 on OpenAlexaff
Yaofei Han, Shaofeng Chen, Zhixun Ma, Chao Gong, Xing Zhao, Yunwei Li

Bibliographic record

VenueIEEE/ASME Transactions on Mechatronics · 2024
Typearticle
Languageen
FieldEngineering
TopicNon-Destructive Testing Techniques
Canadian institutionsUniversity of Alberta
FundersNational Natural Science Foundation of China
KeywordsIdentification (biology)Mode (computer interface)InductanceComputer scienceAlgorithmControl theory (sociology)PhysicsArtificial intelligenceVoltageBiology

Abstract

fetched live from OpenAlex

This letter proposes a sliding mode observer (SMO) based technique to detect thed-,q-axis inductances of interior permanent magnet synchronous machines (IPMSM) offline by using a novel search algorithm. The rationale behind the proposed method is that the chattering effects of the SMO are minimal when the parameters used to construct the observer comply with the real ones. First, a sliding mode flux observer (SMFO) is constructed to observe the rotor flux that has strong links with the inductances. Second, the relationship between the inductances and the observed flux, which is relevant to the chattering effects of the SMFO, is illustrated in theory. Then, a quick search algorithm that can be easily executed on the digital processors is designed to detect thed-andq-axis inductances. Finally, comparative experiment is conducted on an IPMSM to validate the proposed techniques.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.003
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.111
GPT teacher head0.340
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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations0
Published2024
Admission routes1
Has abstractyes

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