Robust control for permanent magnet in-wheel motor in electric vehicles using adaptive fuzzy neural network with inverse system decoupling
Bibliographic record
Abstract
To eliminate the chattering phenomenon and effectively enhance the robustness and dynamic response of the speed control system of a permanent magnet in-wheel motor (PMIWM), a novel decoupling approach is proposed. The speed control system of the PMIWM is analyzed and modeled. By introducing the inverse model into the original PMIWM system, a new decoupling pseudo-linear system is established. A control method based on adaptive fuzzy neural network (AFNN) is investigated to obtain an accurate speed trajectory. The inverse system control approach is introduced into the AFNN-based control system. The PMIWM speed is decoupled completely by the proposed adaptive fuzzy neural network inverse (AFNNI) method. Experiments are carried out on a hardware-in-the-loop (HIL) test bench. Compared with traditional PID control scheme, the proposed AFNNI control strategy can realize a better speed control performance and ensure the robust stability of the PMIWM, even though the motor may suffer from both sudden change in velocity and severe variation under different drive cycles.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".