An Offline Torque Sharing Function for Torque Ripple Reduction in Switched Reluctance Motor Drives
Bibliographic record
Abstract
In this paper, an offline torque sharing function (TSF) for torque ripple reduction in switched reluctance motor (SRM) drives over a wide speed range is proposed. The objective function of an offline TSF is composed of two secondary objectives with a Tikhonov factor to minimize the square of the phase current (copper loss) and derivatives of current references (rate of change of flux linkage). The proposed TSFs with different Tikhonov factors are compared with the conventional TSFs including linear, cubic, and exponential TSFs in terms of efficiency and torque-speed performance while operating in both magnetic linear and saturation regions. Then, the Tikhonov factor is selected based on a tradeoff between the copper loss and torque-speed performance. The maximum torque-ripple-free speed of the selected offline TSF is validated to be seven times as high as the best case in these conventional TSFs. The performance of the offline TSF is verified by simulations and experiments with a 2.3-kW, three-phase 12/8 SRM. Results show that the proposed offline TSF can significantly reduce the torque ripple of SRM without increasing copper loss over a wide speed range.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".