Neuro-fuzzy and fuzzy logic controllers based speed control of IPMSM drive — A torque ripple optimization approach
Bibliographic record
Abstract
This paper presents a closed loop vector control for an interior permanent magnet synchronous motor (IPMSM) drive incorporating a neuro-fuzzy controller (NFC) and a fuzzy logic controller (FLC). The NFC serves as the speed controller of the drive. It is designed so as to generate the appropriate command q-axis current and ensure dynamic speed control. The back-propagation technique is used for the online tuning of this adaptive neuro-fuzzy inference system (ANFIS) parameters. Furthermore, a Mamdani type FLC is designed and incorporated to optimize the developed torque ripple by online adaptation of the hysteresis band limits of the PWM current controller. A performance comparison of the proposed NFC-FLC based IPMSM drive with conventional proportionalintegral (PI) controller based IPMSM drive having fixed hysteresis band limits is provided. Comparative simulation results demonstrate better torque response and dynamic speed performance of the proposed drive at different operating conditions.
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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 | 0.001 | 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".