A New Torque Sharing Function Method for Switched Reluctance Machines With Lower Current Tracking Error
Bibliographic record
Abstract
In this article, a new torque sharing function (TSF) method is proposed for torque ripple reduction in switched reluctance machines (SRMs). The proposed TSF achieves lower current tracking error by establishing a new current reference generation strategy. The phase current reference is first derived from the torque command using offline calculations and also from the phase current response that is obtained from the dynamic model of the SRM. Then, an optimization problem is formulated to shape the current reference for the objective of minimizing the torque ripple and copper losses, while maintaining the required average output torque at the given operating speed. The dynamic simulation of the SRM model is also utilized in the optimization problem. Compared to the existing conventional and optimization-based TSFs, the proposed TSF exhibits lower current tracking error due to the consideration of the current dynamics. Better torque-speed performance with improved average torque and reduced torque ripple is also obtained, especially during commutation. Simulations and experiments under different operating conditions are carried out to verify the proposed TSF method.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".