A Closed-Loop Fuzzy-Logic-Based Current Controller for PMSM Torque Ripple Minimization Using the Magnitude of Speed Harmonic as the Feedback Control Signal
Bibliographic record
Abstract
This paper investigates torque ripple minimization for permanent-magnet synchronous machine (PMSM), and proposes a closed-loop fuzzy-logic-based current controller by using the magnitude of the speed harmonic as the feedback control signal. The speed harmonic can be obtained from machine speed measurement, so the proposed approach does not require accurate machine parameters and is not influenced by the nonlinearity of the machine and drive. The torque harmonic can produce the speed harmonic of the same order, so their relation is investigated, which shows that the magnitude of the speed harmonic is proportional to the magnitude of the torque harmonic of the same order, so it can be used as a measure of torque harmonic for torque ripple minimization. Then, the torque harmonic model is developed to facilitate the design and analysis of the current controller. Afterward, a novel fuzzy-logic-based current controller is proposed to minimize the dominant torque harmonics. The proposed current controller is evaluated on a laboratory PMSM drive system under different load conditions and operation speeds.
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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) | 0.000 | 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".