Performance comparison of a PI and an FLC based tuned PI with adaptive hysteresis controllers for IPMSM drive
Bibliographic record
Abstract
This paper presents the performance comparison of a conventional proportional-integral (PI) controller with a fuzzy logic controller (FLC) based tuned PI controller incorporating an adaptive hysteresis current controller for an interior permanent magnet synchronous motor (IPMSM) drive. For the proposed drive two FLCs are used. One of these is a novel Sugeno type FLC, which is used to tune the PI controller. Thus, the limitations of traditional PI controllers are avoided and the performance of the drive system is improved. Another one is a Mamdani type FLC, which adapts the hysteresis band of the PWM current controller. This adaptation helps to minimize the developed torque ripple. The proposed drive is implemented using DSP board DS1104 for a laboratory 5 hp IPMSM. Comparative simulation and experimental results demonstrate better dynamic response in terms of torque and speed for the proposed drive at different operating conditions.
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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 | 0.000 | 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".