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Adaptive Flux Weakening Controller for Dual Three-Phase PMSM Drives in Vector Space Decomposition

2021· article· en· W3213327083 on OpenAlexaff
Wesam Taha, Diego F. Valencia, Zisui Zhang, Babak Nahid‐Mobarakeh, Ali Emadi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMultilevel Inverters and Converters
Canadian institutionsMcMaster University
Fundersnot available
KeywordsControl theory (sociology)Controller (irrigation)PID controllerComputer scienceAdaptive controlBandwidth (computing)Vector controlDual (grammatical number)VoltageSynchronous motorDigital signal processorPermanent magnet synchronous motorControl engineeringMagnetEngineeringDigital signal processingInduction motorControl (management)Temperature control

Abstract

fetched live from OpenAlex

This paper proposes an adaptive voltage regulation (VR) flux-weakening (FW) control technique for dual three-phase (DTP) permanent magnet synchronous machine (PMSM) drives. Firstly, a small-signal model is developed to demonstrate the need of gain adaptation in order to maintain the controller bandwidth. Moreover, the small-signal model serves as a tuning tool for the FW proportional-integral (PI) controller. Then, the proposed adaptive controller is implemented and tested on a 100 kW DTP-PMSM drive. Simulation results demonstrate an improved drive performance at high speeds when benchmarked against non-adaptive controllers. Furthermore, the adaptive controller offers an extended speed range.

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 score0.946
Threshold uncertainty score0.583

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 head0.018
GPT teacher head0.260
Teacher spread0.242 · 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

Citations11
Published2021
Admission routes1
Has abstractyes

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