Recent advances in direct torque and flux control of IPMSM drives
Bibliographic record
Abstract
This paper presents the latest advancements in direct torque and flux control (DTFC) schemes of interior permanent magnet synchronous motor (IPMSM) drives. A novel eighteen-sector based DTFC scheme incorporating a model based loss minimization algorithm is proposed to mitigate the torque ripples and achieve high efficiency as compared to the conventional six-sector based DTFC scheme. Finally, in order to have direct and better control of reducing the torque/flux ripples further, a nonlinear control incorporating motor electromagnetic developed torque and stator air-gap flux linkage as virtual control variables is developed. In conventional nonlinear controller d-q axis currents (id, iq) are considered as virtual control variables that indirectly controls the torque/flux which may not be suitable for high performance drives. Thus, the proposed work overcomes the major drawback (i.e., torque ripple) of the conventional DTFC based IPMSM drive. Feasibility of the developed DTFC schemes is verified through both simulation and experimental results.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".