Development of a self-tuned neuro-fuzzy controller for induction motor drives
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Bibliographic record
Abstract
In This work a novel adaptive neuro-fuzzy (NF) based speed control of an induction motor (IM) is presented. The proposed neuro-fuzzy controller (NFC) incorporates fuzzy logic laws with a five-layer artificial neural network (ANN) scheme. In this controller only three membership functions are used for each input keeping in mind for low computational burden, which will be suitable for real-time implementation. Furthermore, for the proposed NFC an improved self-tuning method is developed based on the IM theory and its high performance requirements. The main task of the tuning method is to adjust the parameters of the fuzzy logic controller (FLC) in order to minimize the square of the error between actual and reference outputs. This work also demonstrates how the proposed NFC can easily be adjusted to work with different size of induction motors. A complete simulation model for indirect field oriented control of IM incorporating the proposed NFC is developed. The performance of the proposed NFC based IM drive is investigated extensively at different operating conditions in simulation. In order to prove the superiority of the proposed NFC, the results for the proposed controller are also compared to those obtained by a conventional PI controller. The proposed NFC based IM drive is found to be more robust as compared to conventional PI controller based drive and hence found suitable for high performance industrial drive applications.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 it