A Fixed-Switching-Frequency Integral Sliding Mode Current Controller for Switched Reluctance Motor Drives
Bibliographic record
Abstract
A fixed-switching-frequency model-based sliding mode current controller with an integral switching surface for switched reluctance motor (SRM) drives is presented in this paper. Based on the equivalent circuit model of SRM, including the magnetic saturation and mutual coupling, an integral sliding mode current controller is designed. The stability of sliding mode controller is analyzed in two scenarios: 1) one with known motor parameters and 2) the other with bounded modeling error. In order to analyze the robustness of the sliding mode controller, the motor controller parameter constraints are derived and the stability analysis is demonstrated by considering motor parameter modeling errors. The sliding mode controller is validated by both simulation and experimental results with a 2.3-kW, 6000-r/min, three-phase 12/8 SRM over the wide speed range in both linear and magnetic saturation regions. Compared to the hysteresis current controller, the sliding mode controller demonstrates comparable transient response and steady-state response in terms of torque ripples, current ripples, root-mean-square error of current and torque. Moreover, the sliding mode controller has some advantages over the hysteresis controller, including constant switching frequency and much lower sampling rate.
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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 | 0.001 |
| 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) | 0.002 | 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".