Model reference adaptive flux observer based neuro-fuzzy controller for induction motor drive
Bibliographic record
Abstract
This paper presents a model reference adaptive flux (MRAF) observer based neuro-fuzzy controller (NFC) for an induction motor (EM) drive. An improved observer model is developed based on a reference flux model and a closed-loop Gopinath model flux observer which combines current and voltage model flux observers. The d-axis reference flux linkage of the indirect field oriented control is provided by flux weakening method. Furthermore, a proportional-integral (PI) based flux controller is used to provide the compensation for the reference flux model by comparing the flux reference and the observed flux from Gopinath model flux observer. An improved self-tuned NFC is utilized as a speed controller for IM drive. The proposed NFC incorporates fuzzy logic laws with a five-layer artificial neural network (ANN) scheme. In the proposed NFC, parameters of the 4th layer are tuning online for the purpose of minimizing the square of the error. Furthermore, the design of normalized inputs make the proposed NFC suitable for variant size of IM with a little adjusting. A complete simulation model for indirect field oriented control of IM incorporating the proposed MRAF observer based NFC is developed in Matlab/Simulink. The performances of the proposed IM drive is investigated extensively at different dynamic operating conditions such as step change in load, step change in change in speed, parameter variations, etc. The performance of the proposed MRAF observer based NFC controller is found robust and potential candidate for high performance industrial drive applications.
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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 | 0.001 | 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".