Finite Control Set Model Predictive Control for Switched Reluctance Motor Drives with Reduced Torque Tracking Error
Bibliographic record
Abstract
In this paper, a new method to reduce the steady state torque tracking error of finite control set model predictive torque control for switched reluctance motor drives is proposed. The steady state tracking error is considered as one of the main shortcomings of the conventional Finite Control Set Model Predictive Control (FCS-MPC). This can happen due to parameter uncertainties or when the multiple objectives are achieved by a single function with weighting factors. In the conventional model predictive torque control for SRM, the control action is obtained by a multi-objective cost function designed to track a reference torque while minimizing the phase currents over the prediction horizon. The optimal switching state which minimizes the cost function is selected and applied at each switching instant, which results in the steady state torque tracking error. In this paper, a compensation term is added to the reference torque at each sample instant to minimize the torque tracking error. The compensation term is calculated based on the estimated average torque tracking error in the previous sample times. Simulations on a three phase, 12/8, 2.3 kW SRM show promising results with the proposed method as compared to the conventional FCS-MPC.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".