An Adaptive-Filter-Based Torque-Ripple Minimization of a Fuzzy-Logic Controller for Speed Control of IPM Motor Drives
Bibliographic record
Abstract
This paper presents an adaptive-filter-based torque-ripple minimization (TRM) of a fuzzy-logic controller (FLC) for speed control of an interior permanent magnet (IPM) motor drive. A simple and effective first-order digital infinite impulse response filter is utilized to reduce the torque ripples introduced by the FLC. The gain of the filter is adapted online based on the magnitude of the torque ripple. The optimal position of the filter in the complete drive is also determined for effective TRM. The various sources of torque pulsations in a practical electric-machine drive are described. The main causes of the torque ripple in an FLC are also explained. A simulation model for closed vector control of an FLC-based IPM motor drive incorporating the proposed TRM technique is developed in Matlab/Simulink. The complete drive is also experimentally implemented using digital signal processor board DS1102 for a laboratory 1-hp IPM motor. The effectiveness of the proposed technique is investigated, both in simulation and experiment, at different operating conditions. It is found that the performance of the FLC-based IPM drive is improved significantly with the proposed TRM technique.
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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) | 0.000 | 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".