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Record W2060463109 · doi:10.1109/iecon.2010.5675179

Effective FPGA-based electric motor modeling with floating-point cores

2010· article· en· W2060463109 on OpenAlex

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
TopicReal-time simulation and control systems
Canadian institutionsOpal-Rt Technologies (Canada)Polytechnique Montréal
Fundersnot available
KeywordsField-programmable gate arrayComputer scienceComputationFloating pointPoint (geometry)Real-time simulationEmbedded systemInduction motorVoltageEngineeringElectrical engineering

Abstract

fetched live from OpenAlex

The simulation of electromechanical systems like motor drives often requires sub-microsecond calculation timesteps considering the fast dynamic of such systems and the high-switching frequency involved. Migrating computational load to an FPGA processor has proven to effectively meet the real-time simulation needs of such systems. However, many challenges still must be overcome before broad adoption of FPGA technology for real-time simulation applications occurs. In this paper, a general framework is presented for effective use of FPGA machine drive modeling when the state-space approach is used. Computations are performed in floating-point using commercially available arithmetic cores. Using the discussed framework guarantees that time steps well below 1 μs can be achieved. Two real-world applications examples are given in the paper: an FPGA-based implementation of a BLDC motor, and an FPGA-based implementation of an induction motor.

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: Empirical
Teacher disagreement score0.406
Threshold uncertainty score0.442

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 head0.003
GPT teacher head0.184
Teacher spread0.181 · 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

Citations32
Published2010
Admission routes1
Has abstractyes

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