Improved multiple vector model predictive torque control of permanent magnet synchronous motor for reducing torque ripple
Bibliographic record
Abstract
Abstract The two‐voltage‐vector (2VV) model predictive torque control (MPTC) has been widely discussed and compared to the conventional single‐voltage‐vector (1VV) MPTC in permanent magnet synchronous motor drive applications, where it was shown to have quick response and low torque ripple. An improved 2VV‐MPTC is set forth based on extended control set. In the proposed approach, the reference voltage vector is calculated using the established torque and flux deadbeat control and optimal calculation of the duty cycles. Moreover, to further improve the torque performance, the proposed algorithm is extended to three‐voltage vector (3VV), which is shown to achieve even better performance. Experimental results demonstrate that the proposed 2VV and 3VV‐MPTC algorithms have better computation efficiency and can significantly reduce torque ripple compared to the previous methods.
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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".