Comparative analysis of intelligent controllers for high performance interior permanent magnet synchronous motor drive systems
Bibliographic record
Abstract
This paper provides a comparison among different intelligent controllers, particularly, fuzzy logic (FL), artificial neural network (ANN) and neuro-fuzzy (NF) controllers in terms of designing approach, implementation and performance for interior permanent magnet synchronous motor (IPMSM) drives. A radial basis function network (RBFN) is utilized as an ANN in this work. For NF control a fuzzy basis function network (FBFN) is developed in which the FL concepts are embedded. In order to provide a comparison, a closed loop vector control scheme for IPMSM incorporating intelligent controllers is successfully implemented in real-time using digital signal processor (DSP) board DS1102. The performances of various intelligent controllers are investigated and compared both in simulation and experiment. A review of intelligent controller applications for motor drive systems is also presented in this paper. Thus, this paper provides useful information for researchers and practicing engineers about intelligent controller applications for IPMSM drives.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".