Comparison of efficiency for a PI and a FLC based IPMSM drive incorporating loss minimization algorithm over wide speed range
Bibliographic record
Abstract
This paper presents a comparison in efficiency between a fuzzy logic controller (FLC) and a proportional-integral (PI) controller based interior permanent magnet synchronous motor (IPMSM) drive incorporating an online loss minimization algorithm (LMA). The LMA is developed based on the motor model. In order to maximize the operating efficiency the d-axis armature current is controlled optimally based on the developed LMA. A novel fuzzy logic controller (FLC) is developed, in such a way that it can simultaneously control both torque and flux of the motor while maintaining current and voltage constraints. Thus, the FLC extends the operating speed limits for the motor. The LMA is incorporated with the FLC so that the motor can operate over a wide speed range while maintaining the high efficiency. A performance comparison of the LMA based IPMSM drive with FLC and PI controller is provided. Simulation results demonstrate the higher efficiency and better dynamic response of the FLC based drive as compared to the PI controller over a wide speed range. The complete drive is also experimentally implemented using DSP board DS1104 although the complete experimental tests are yet to be done.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".