Online Efficiency Optimization of a Fuzzy-Logic-Controller-Based IPMSM Drive
Bibliographic record
Abstract
This paper presents an online loss-minimization algorithm (LMA) for a fuzzy-logic-controller (FLC)-based interior permanent-magnet synchronous-motor (IPMSM) drive to yield high efficiency and high dynamic performance over a wide speed range. LMA is developed based on the motor model. In order to minimize the controllable electrical losses of the motor and thereby maximize the operating efficiency, thed-axis armature current is controlled optimally according to the operating speed and load conditions. For vector-control purpose, FLC is used as a speed controller, which enables the utilization of the reluctance torque to achieve high dynamic performance as well as to operate the motor over a wide speed range. In order to test the performance of the proposed drive in real time, the complete drive is experimentally implemented using DSP board DS1104 for a prototype laboratory 5-hp motor. The performance of the proposed loss-minimization-based FLC for IPMSM drive is tested in both simulation and experiment at different operating conditions. A performance comparison of the drive with and without the proposed LMA-based FLC is also provided. It is found from the results that the proposed LMA and FLC-based drive demonstrates higher efficiency and better dynamic responses over FLC-based IPMSM drive without LMA.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 | 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".