Fuzzy logic based efficiency optimization and improvement of dynamic performance of IPM synchronous motor drive
Bibliographic record
Abstract
This paper presents fuzzy logic controllers (FLCs) based both efficiency optimization and high performance speed control of an interior permanent magnet synchronous motor (IPMSM) drive. In order to maximize the efficiency during both transient-state and steady-state operations while meeting the speed and load torque demands two fuzzy logic based efficiency controllers are designed to generate the optimum magnetizing current, which is d-axis component of the stator current, id. The steady-state fuzzy efficiency controller (SSFEC) is a search controller operating in steady-state to minimize the drive power losses to achieve higher efficiency by reducing the stator flux. The transient-state fuzzy efficiency controller (TSFEC) is a controller operating during transient-state to increase the flux, depending on the speed error and its derivative to let the drive track the reference command. In order to achieve high dynamic performance another FLC is used which controls the torque component of the stator current based on speed error and its derivative. Furthermore, a torque compensator is used to reduce the torque and speed ripples. The efficacy of the proposed IPMSM drive for efficiency optimization, and robustness is tested in both simulation and experiment.
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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 | 0.000 | 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 | 0.000 | 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".