Closed-loop control of virtual FPGA-coded permanent magnet synchronous motor drives using a rapidly prototyped controller
Bibliographic record
Abstract
Presented in this paper are the results of closed-loop control experiments using a virtual permanent magnet synchronous motor (PMSM) drive implemented on a field-programmable gate array (FPGA) card connected to an external controller. The FPGA-based PMSM motor drive is implemented on an eDRIVEsim simulator, based on the RT-LAB platform. The eDRIVEsim simulator implements 2 types of motor drive models, Park (d-q) and Finite Element Analysis (FEA), on an FPGA card of the simulator. The FPGA-based motor model is designed with Xilinx System Generator (XSG) blockset with no HDL hand coding. Both motor models compute motor currents using a phase-domain algorithm solver that can take into account the instantaneous variation of inductance and non-sinusoidal induced voltage. The FEA-type model uses inductance and Back-EMF profiles computed with JMAG-RT. The d-q model uses sinusoidal induced Back-EMF voltage and phase inductance values computed from Ld and Lq using the well-known Park transformation. A 3-phase IGBT inverter implemented in the FPGA chip drives the PMSM machine. The PWM controller is designed using Rapid Control Prototyping (RCP) methodology based on Simulink. It is implemented on an separate RT-LAB system using standard Opal-RT FPGA-based I/O cards for Analog Input capture and PWM generation. The paper presents results from the closed-loop control of the PMSM drive in both current control and speed control modes and discusses the advantages of using such a virtual test bench for motor drives.
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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) | 0.002 | 0.000 |
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.
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".