Extending the Frame-Angle-Based Direct Torque Control of PMSM Drives to Low-Speed Operation
Bibliographic record
Abstract
This paper extends the application of a frame-angle-based (FAB) direct torque controller (DTC) for permanent magnet synchronous motor (PMSM) drives to low and very low speeds. The FAB-DTC is intended for PMSM drives that are fed by 3φ six-pulse wavelet-modulated dc-ac converters. The foundations of the FAB-DTC are based on regulating the d-q-axis stator voltages, in response to variations in the load torque and/or drive speed. The adjustments in d-q-axis stator voltages are created by changing the angle 9 of the frame spanned by the d-q-axis stator voltages. In order to extend the FAB-DTC to low and very low speeds, the changes in 9 are accompanied by changes in the maximum scale. The values of maximum scale and d-q-axis stator voltages are used to update or change the reference signals employed by the wavelet modulation to generate switching pulses to operate a 3φ dc-ac converter. The complete PMSM drive system incorporating the modified FAB-DTC is implemented for a 10-hp PMSM drive system. The performance of the FAB-DTC is investigated for different changes in the command torque for various low and very low speeds. Performance results demonstrate stable, fast, dynamic, and accurate responses, which have minor sensitivity to variations in load torque and/or drive speed.
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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 | 0.000 |
| 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) | 0.001 | 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".