A PWM Fixed-Gain Super-Twisting Sliding Mode Current Controller for Switched Reluctance Motors
Bibliographic record
Abstract
Proper current control is essential in SRM drives in order to ensure adequate reference tracking and torque ripple reduction when using current profiling techniques. In this context, this paper proposes a PWM super-twisting sliding mode current controller for switched reluctance motors with a fixed-gain structure. The approach presents a model-free structure, not requiring model information on implementation. The PWM implementation ensures a fixed switching frequency and the fixed-gain approach leads to a simple control structure, not demanding any sort of gain lookup tables. The complex task of gain design for super-twisting controllers is solved in this paper by means of an optimization-based design methodology, using the sum of the squared error as a cost function. The proposed technique is compared to a higher sampling hysteresis controller in terms of current and torque root-mean-square error. Simulation results are presented, showing that the proposal achieves a performance comparable to a higher sampling hysteresis controller.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".