Development of a self-tuned neuro-fuzzy controller for induction motor drives
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".