Fixed-switching Model-free Predictive Current Control of Switched Reluctance Motor Using Parameter Estimation
Bibliographic record
Abstract
Model predictive control (MPC) is a widely known control strategy used in power electronics and electric drives fields. Its application to switched reluctance motors (SRM) is particularly attractive given MPC’s ability to handle nonlinearities while providing a high dynamic response. However, due to the dependence of MPC on the motor parameters, current tracking can be degraded as electrical parameters can change in different operating regions or when there is a mismatch in model information collected from finite element analysis. To address these issues, this paper proposes a fixed-switching model-free predictive current control (MFPC). The proposed MFPC maintains a tuned duty cycle that enables a recursive least square estimation method to estimate inductance and back-EMF values used in the predictive controller algorithm. The effectiveness of the proposed MFPC has been verified through the simulation results and compared with a proportional-integral controller. The results show improved current tracking performance and lower torque ripple using MFPC approach, in the low to medium speed range.
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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 | 0.000 | 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) | 0.000 | 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".