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Record W2786083206 · doi:10.1109/epec.2017.8286139

Grid search optimization techniques for the indirect vector-controlled induction motor drives

2017· article· en· W2786083206 on OpenAlex
F. Lftisi, Glyn George, Casey Butt, Adel Aktaibi, M.A. Rahman

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
TopicSensorless Control of Electric Motors
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsControl theory (sociology)Induction motorRobustness (evolution)Vector controlComputer scienceGSMPID controllerGridTracking errorControl engineeringEngineeringMathematicsControl (management)Voltage

Abstract

fetched live from OpenAlex

In this paper, a Grid Search Method (GSM) with a Field Oriented Control (FOC) is presented to determine the optimal Proportional-Integral (PI) controller gain parameters for the speed control of an induction motor. This technique is useful in finding the optimal controller gain (proportional and integral) in a closed loop system. The GSM is used to obtain gains required for the adaptive PI by seeking the optimal local minimum of a squared speed error function. The results show that the GSM is very efficient, and gives stable convergence characteristics. It has a good track record of robustness under load variation, fast dynamic response, and high precision in tracking desired speed. Furthermore, the control system, based on the GSM-PI algorithm in FOC for induction motors, can be readily implemented for occasions of frequent load changing, such as in industrial applications.

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: Empirical · Consensus signal: none
Teacher disagreement score0.913
Threshold uncertainty score0.429

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 head0.252
Teacher spread0.234 · 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

Citations4
Published2017
Admission routes1
Has abstractyes

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