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Record W1991008436 · doi:10.1109/speedham.2008.4581095

Real-time simulation on FPGA of a permanent magnet synchronous machine drive using a finite-element based model

2008· article· en· W1991008436 on OpenAlexaff
Christian Dufour, Jean Bélanger, Vincent Lapointe, Simon Abourida

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicReal-time simulation and control systems
Canadian institutionsOpal-Rt Technologies (Canada)
Fundersnot available
KeywordsSettling timeField-programmable gate arrayComputer scienceFinite element methodReal-time simulationPulse-width modulationSynchronous motorInverterInsulated-gate bipolar transistorControl theory (sociology)Embedded systemVoltageControl engineeringEngineeringElectrical engineeringStep response

Abstract

fetched live from OpenAlex

This paper presents the development of high fidelity real-time model of a permanent magnet synchronous motor (PMSM) drive, to be used in hardware-in-the-loop (HIL) application. The PMSM model is based on a finite-element analysis (FEA) method and implemented on an FPGA chip. The motor inductances and flux maps are computed from the JMAG-RT finite element analysis software. A 3-phase IGBT inverter drives the PMSM machine. Both models are designed in Simulink using the Xilinx Blockset and then automatically compiled and loaded on the FPGA using the RT-LAB real-time simulation platform. The complete PMSM drive model runs with an equivalent 10 nanosecond time step and has a latency of 300 ns (PMSM machine and inverter), with a total HIL delay from external PWM inputs to analog outputs with 1 microsecond settling time on the FPGA of just 1.3 microseconds.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.019

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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.001

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 head0.017
GPT teacher head0.237
Teacher spread0.220 · 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

Citations19
Published2008
Admission routes1
Has abstractyes

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