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Record W2139713399 · doi:10.1109/pes.2010.5589714

FLC based tuned PI controller for wide speed range operation of IPMSM drive

2010· article· en· W2139713399 on OpenAlexaff
Ronald S. Rebeiro, M. Nasir Uddin

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSensorless Control of Electric Motors
Canadian institutionsLakehead University
Fundersnot available
KeywordsControl theory (sociology)TorqueController (irrigation)Electronic speed controlPID controllerComputer scienceRange (aeronautics)Direct torque controlMotor driveVoltageEngineeringControl engineeringPhysicsInduction motorControl (management)Electrical engineeringTemperature control

Abstract

fetched live from OpenAlex

This paper presents a fuzzy logic controller (FLC) based tuned proportional-integral (PI) controller for interior permanent magnet synchronous motor (IPMSM) drive. The PI controller serves as the primary speed controller whose gains are tuned by a novel Sugeno type FLC. Thus, the limitations of traditional PI controllers are avoided and the performance of the drive system is improved. A flux controller is also incorporated in such a way that both torque and flux of the motor can be controlled while maintaining current and voltage constraints. The flux controller is designed based on maximum-torque-per-ampere (MTPA) operation below the rated speed and flux weakening operation above the rated speed. Thus the proposed drive extends the operating speed limits for the motor and enables the appropriate use of the reluctance torque. A performance comparison of the proposed IPMSM drive with conventional PI controller based drive is provided. Simulation results demonstrate the better dynamic response of the proposed drive over a wide 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 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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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 science0.0010.000
Research integrity0.0000.000
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 head0.006
GPT teacher head0.203
Teacher spread0.197 · 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 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

Citations5
Published2010
Admission routes1
Has abstractyes

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