Neuro-fuzzy and fuzzy logic controllers based speed control of IPMSM drive — A torque ripple optimization approach
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper presents a closed loop vector control for an interior permanent magnet synchronous motor (IPMSM) drive incorporating a neuro-fuzzy controller (NFC) and a fuzzy logic controller (FLC). The NFC serves as the speed controller of the drive. It is designed so as to generate the appropriate command q-axis current and ensure dynamic speed control. The back-propagation technique is used for the online tuning of this adaptive neuro-fuzzy inference system (ANFIS) parameters. Furthermore, a Mamdani type FLC is designed and incorporated to optimize the developed torque ripple by online adaptation of the hysteresis band limits of the PWM current controller. A performance comparison of the proposed NFC-FLC based IPMSM drive with conventional proportionalintegral (PI) controller based IPMSM drive having fixed hysteresis band limits is provided. Comparative simulation results demonstrate better torque response and dynamic speed performance of the proposed drive at different operating conditions.
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.
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) | 0.001 | 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 it