Model reference adaptive fuzzy controller and fuzzy estimator for high performance induction motor drives
Bibliographic record
Abstract
This paper presents the adaptive speed controller and rotor resistance estimator based on the fuzzy logic approach for a high-performance indirect vector-controlled induction motor drive. In the proposed system, fuzzy logic principle is first utilized for the control of rotor flux and speed. A model-reference adaptive scheme is then proposed in which the adaptation mechanism is executed using fuzzy logic. In order to achieve the decoupling control of flux and torque, a fuzzy logic rotor resistance estimator is designed. Key information used for this estimator is a function of stator frequency, stator currents and derivation of rotor flux. The error between its estimated value and actual value as well as error derivation are employed as fuzzy estimator's inputs. The performance of proposed fuzzy logic control system is evaluated by computer simulation for various operating conditions using the parameters of a laboratory 2.2 kVA induction motor drive. The implementation of this control system is considered and discussed.
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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 | 0.001 | 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".