Smart Controls for Switched Reluctance Motor 8/6 Used for Electric Vehicles Underground Mining Security
Bibliographic record
Abstract
Switched reluctance motors (SRM) are a type of electromagnetic machine that has piqued the interest of manufacturers, as opposed to induction, brushless, or permanent magnet machines.This is because the rotor is simple, robust, and lacks coils, windings, and permanent magnets.It can also operate in a wide range of power in the electric vehicle's drive, even in extreme conditions such as underground mines, ensuring a longer life of service.However, due to the toothed shape of the rotor, the SRM is characterized by vibration and acoustic noise.To solve this problem to better adapt the SRM to the electric vehicle, we propose to use intelligent techniques such as the controller (ANN) and the fractional order controller (PI α ).This article compares two intelligent speed controllers that use direct torque control (DTC) to reduce torque ripples.As a result, when associated with direct torque control, the Fractional Order Controller (PI α ) outperforms the Artificial Neural Network (ANN).
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".