Filter based torque ripple minimization of an adaptive neuro-fuzzy controller for induction motor
Bibliographic record
Abstract
A novel filter based adaptive neuro-fuzzy (NF) speed controller coupled with an adaptive flux regulation for indirect field oriented induction motor (IM) drives is presented in this paper. A first order low pass digital infinite impulse response (IIR) filter is used in the input side of the proposed NF speed controller, which significantly softens the torque command generated by the adaptive neuro-fuzzy controller (NFC). Furthermore, the low pass filter based neuro-fuzzy controller (NFC) is able to eliminate the slight stable-state error of the speed, which the non-filter NFC based motor drive suffers from. In order to minimize the stable-state torque ripple of the induction motor, a speed error based reference flux regulation is employed in the proposed control scheme for induction motor drives. When the rotor speed is following the reference speed and kept in stable-state, the reference flux is moderately decreased, so that the torque ripple is minimized, and vice versa, the reference flux is increased up to the rated value when the speed error overruns. A complete simulation model for indirect field oriented control of IM incorporating the proposed filter based NFC and reference flux regulation is developed in Matlab/Simulink. The performance of the drive has been tested at different operating conditions. It has been proved that the proposed filter based neuro-fuzzy controller coupled with the torque ripple minimization method is suitable for application in high performance motor drive system.
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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".