Evaluation of Direct Torque Predictive Control for SRM with Reduced Computation Resources
Bibliographic record
Abstract
Switched reluctance motors are considered prime candidates for electric vehicle applications due to their numerous advantages, especially the absence of permanent magnets. However, controlling switched reluctance motors poses significant challenges due to their high levels of nonlinearity. In this work, a direct torque predictive control algorithm is proposed. This method requires fewer parameters to be tuned compared to indirect torque control and involves fewer offline steps, thereby reducing both memory and computational demands while offering better dynamic robustness. Typically a finite control set model predictive controller for a three-phase switched reluctance motor drive evaluates 27 switching states at each control instant, but the proposed approach reduces this number to 9. Consequently, the method is more computationally and memory efficient, making it practical for industrial applications. By incorporating a modified cost function, the controller demonstrates excellent performance at lower speeds across various metrics. Nonetheless, challenges at higher speeds are identified, particularly regarding the controller's limitations in achieving advanced commutation due to the generation of slight negative torque at specific electrical positions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.001 | 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".