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A Bi-subspace Model Predictive Controller Based on Incremental Model for the Dual Three-phase PMSM Drives

2024· article· en· W4404036116 on OpenAlex
Jingru Yang, Pedro F. C. Gonçalves, Subarni Pradhan, Babak Nahid‐Mobarakeh

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.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Control Systems Optimization
Canadian institutionsMcMaster University
Fundersnot available
KeywordsControl theory (sociology)Dual (grammatical number)Subspace topologyModel predictive controlComputer scienceController (irrigation)Phase (matter)Control engineeringEngineeringArtificial intelligenceControl (management)Physics

Abstract

fetched live from OpenAlex

The presence of current harmonics stemming from the permanent magnet (PM) flux linkage harmonics poses a significant challenge in dual three-phase permanent magnet synchronous motor drives. In this paper, we propose an incremental model based on a bi-subspace virtual vector predictive current controller. Compared to the conventional model-based methods, the incremental model does not rely on prior knowledge of the PM flux linkage parameter, which can vary according to the operating conditions of the drive. Moreover, by incorporating a higher number of data steps in the prediction, the proposed incremental model ensures more accurate tracking of the reference currents and minimizes current harmonics induced by uncertainties in the PM flux linkage. Based on simulation results, this digest provides a comparison between the conventional and the incremental model-based bi-subspace virtual vector predictive current control methods.

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)0.000
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: Methods · Consensus signal: none
Teacher disagreement score0.984
Threshold uncertainty score0.661

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 head0.015
GPT teacher head0.255
Teacher spread0.241 · 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

Quick stats

Citations2
Published2024
Admission routes1
Has abstractyes

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