Performance Analysis of an FLC Based Online Adaptation of Both Hysteresis and PI Controllers for IPMSM Drive
Bibliographic record
Abstract
This paper presents a closed loop vector control of an interior permanent magnet synchronous motor (IPMSM) drive incorporating two separate fuzzy logic controllers (FLCs). The first one is a Mamdani type FLC which minimizes the developed torque ripple by varying online the hysteresis band of the PWM current controller. The second one is an FLC based tuned proportional-integral (PI) controller where the PI controller actually serves as the primary speed controller and its gains are tuned by a novel Sugeno type FLC. Thus, the limitations of traditional PI controllers are avoided and the performance of the drive system is improved. A flux controller is also incorporated in such a way that both torque and flux of the motor can be controlled while maintaining current and voltage constraints. Thus the proposed drive extends the operating speed limits for the motor and enables the appropriate use of the reluctance torque. The proposed IPMSM drive is also implemented in real-time using DSP board DS1104 for a laboratory 5 hp motor. A performance comparison of the proposed IPMSM drive with conventional PI controller based drive is provided. Simulation and experimental results demonstrate the better dynamic response in torque and speed for the proposed drive over a wide speed range.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".