A Review of Predictive Control Techniques for Switched Reluctance Machine Drives. Part I: Fundamentals and Current Control
Why this work is in the frame
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Bibliographic record
Abstract
This two-part paper presents a review of the predictive control techniques applied in switched reluctance machine (SRM) drives. The objective is to promote the applications of predictive control-based strategies in these machines, given its potential to develop high-performance operation and make SRM more suitable for practical scenarios. Part I of this survey presents all fundamental concepts of SRM drives, predictive control and the adopted classification, and a literature review of predictive current control (PCC) strategies. The control techniques are analyzed according to their modelling approach, switching behaviour and calculation of optimal input. A performance comparison is also presented, and the current challenges, improvement opportunities and future trends of PCC in SRM are discussed.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| 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) | 0.000 | 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 it