An Improved Torque Sharing Function for Torque Ripple Reduction in Switched Reluctance Machines
Bibliographic record
Abstract
An offline torque sharing function (TSF) is introduced in this paper for torque ripple reduction in switched reluctance machines (SRM). This TSF uses static flux linkage characteristics of the machine obtained from finite element analysis or experiments that describe the machine dynamics to determine optimal current profiles such that the torque ripple reduction is achieved with minimal copper losses. Due to this feature, the proposed TSF performs well across a wide speed range. Additionally, the objective function of the proposed TSF uses only one weight parameter, which facilitates the use of this TSF. In this paper, an intuitive justification for the selection of this weight parameter is given, and the performance of this TSF is validated in simulation and experimentally on a 5.2 kW, four phase SRM. To baseline its performance, the proposed TSF has been compared to the offline TSF in the literature, which shows that it has better current tracking performance at higher speeds due to the inclusion of flux linkage characteristics. Finally, it has been compared to conduction angle control at speeds above the base speed to show that it can be a viable alternative for the control of SRM even in an operation region normally not considered for TSF.
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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 | 0.001 |
| 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) | 0.002 | 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".