Fuzzy-Logic-Based Control for Induction Motor Drive with the Consideration of Core Loss
Bibliographic record
Abstract
This paper presents a novel speed control technique of induction motor (IM) drive with the consideration of core loss. Previous works on field-oriented control of induction motor mainly focus on the simplified equivalent circuit by neglecting the core loss. Thus the designed PI and fuzzy constants of the controller without core loss may not work properly under the wide operating range, such as variation of load, variation of reference speed, variation of reference flux, etc. Considering the real time effect of core loss, the present work formulates the non-linear model of the induction motor drive. The complete vector control scheme of the IM drive incorporating the FLC is simulated for a squirrel-cage IM using Matlab/Simulink. The performance of the proposed FLC-based IM drive is investigated and compared to those obtained from the conventional proportional-integral (PI) controller-based drive at various dynamic operating conditions, such as, sudden change in command speed, step change in load, etc. The obtained results confirm the effectiveness of the vector controlled induction motor drive system with the consideration of core loss.
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 | 0.000 | 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".