Optimal Current Control of Switched Reluctance Motors Over the Entire Operating Range
Bibliographic record
Abstract
Controlling Switched Reluctance Motors (SRMs) requires careful selection of conduction angles to ensure optimal performance under various operating conditions. At low speeds, hysteresis control can provide the required torque by selecting the appropriate current reference. However, torque quality and efficiency can be further improved by selecting the right conduction angles. In contrast, conduction angles are the only control parameters at high speeds since peak current control is impossible. Hence, choosing the right conduction angles can significantly impact the average torque, torque ripple, and the RMS value of phase current, loss, and efficiency. This paper discusses the effects of turn-on and turn-off angles on average torque, torque ripple, and Integral Time Absolute Error (ITAE). Genetic Algorithm (GA) is used to find optimized firing angles, and the objective function for optimization is maximizing the average torque. The paper proposes a comprehensive approach to automatically control the conduction angles that excite SRMs at variable speeds, reference currents, and DC-link voltages. The results of the simulation demonstrate the efficacy of this method. Furthermore, the proposed controls are evaluated in a three-phase 12/8 SRM compared to a conventional controller. This controller is particularly suitable for transportation applications, especially for traction and propulsion in vehicles, due to its high average torque, low torque ripple, and fast dynamic response across various operating points.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".