Sensorless ANFIS-Based Control of PV-Powered Double Stator Induction Motors for EVs
Bibliographic record
Abstract
This paper propose a robust ANFIS based on Luenberger observer for sensor-less control of DSIM drives via backstepping control (BC) and supplied by a photovoltaic solar.The maximum power delivery to the DSIM is achieved by boost converter which employs P&O MPPT controller.It adjusts the duty cycle of the three-level DC-DC boost converter for extracting maximum power from PV array.In addition, ANFIS is used in this study to ameliorate the performance of this sensor-less control.However, to enhance the performances of this control, we used an EV based dynamic emulator.Thus, we sized and tested the global system under different metrological conditions.In Addition, we have highlighted the comparative analysis between the suggested ANFIS-LO and conventional observer.Finally, the obtained results show the efficiency of the suggested control scheme and the improvements of ANFIS controller based on LO in a DSIM drive system.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".