A novel genetic algorithm based fuzzy logic controller for IPM synchronous motor drive
Bibliographic record
Abstract
This paper presents a novel speed control scheme using a new genetic-based fuzzy logic controller (GFLC) for an interior permanent magnet synchronous motor (IPMSM) drive. The proposed GFLC is designed to have less computational burden, which makes it suitable for online implementation. The parameters for the GFLC are tuned by genetic algorithm (GA). The complete vector control scheme incorporating the GFLC is successfully implemented in real time using a digital signal processor board DS 1102 for a laboratory 1 hp interior permanent magnet motor. The efficacy of the proposed GFLC based IPMSM drive is verified by simulation as well as experimental results at different dynamic operating conditions such as sudden load change, parameter variations, step change of command speed, etc. The proposed fuzzy logic controller is found to be a robust controller for application in IPMSM drive.
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How this classification was reachedexpand
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) | 0.000 | 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".