Nested Optimization-Based Current Shaping for Torque Ripple and Losses Mitigation in SRMs
Bibliographic record
Abstract
Switched reluctance motors (SRMs) have emerged as an attractive alternative due to features such as the absence of circulating current in the rotor and the fact that no permanent magnets are required. However, their inherent structural characteristics and the resulting nonlinear effects lead to pronounced torque ripple, which has significantly hindered their adoption in industrial applications and electric vehicles. As a novel approach, this paper proposes a nested optimizationbased current shaping strategy for SRMs in order to mitigate torque ripple and copper losses. The proposal combines NSGA-II for optimizing firing angles with a Quadratic Programming (QP) algorithm that minimizes instantaneous torque tracking error under flux variation constraints. This hybrid approach balances efficiency and torque smoothness, especially at medium to high speeds, by compensating for negative torque generated due to tail currents. The optimal trade-off among Pareto-optimal solutions is selected based on the minimum Euclidean distance to the ideal point. Experimental validation on a$12 / 8$SRM under diverse operating conditions confirms the effectiveness of the method, showing significant improvements in torque quality compared to the conventional sinusoidal TSF. Additionally, the approach offers a practical implementation with reduced design complexity, enhancing its applicability in advanced SRM control strategies.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".