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.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".