Real-time simulation of finite-element analysis permanent magnet synchronous machine drives on a FPGA card
Bibliographic record
Abstract
This paper presents a real-time simulator of a permanent magnet synchronous motor (PMSM) drive based on a finite- element analysis (FEA) method and implemented on an FPGA card for HIL testing of motor drive controllers. The proposed PMSM model is a phase domain model with inductances and flux profiles computed from the JMAG-RT finite element analysis software. A 3-phase IGBT inverter drives the PMSM machine. Both models are implemented on an FPGA chip, with no VHDL coding, using the RT-LAB real-time simulation platform from Opal-RT and a Simulink blockset called Xilinx System Generator (XSG). The PMSM drive, along with an open-loop test source for the pulse width modulation, is coded for an FPGA card. The PMSM drive is completed with various encoder models (quadrature, Hall effects and resolver). The overall model compilation and simulation is entirely automated by RT-LAB. The drive is designed to run in a closed loop with a HIL-interfaced controller connected to the I/O of the real-time simulator. The PMSM drive model runs with an equivalent 10 nanosecond time step (100 MHz FPGA card) and has a latency of 300 ns (PMSM machine and inverter) with the exception of the FEA-computed inductance matrix routines which are updated in parallel on a CPU of the real-time simulator at a 40 us rate. The motor drive is directly connected to digital inputs and analog outputs with 1 microsecond settling time on the FPGA card and has a resulting total hardware-in-the-loop latency of 1.3 microseconds.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".