Sur la commande tolérante aux défauts des machines asynchrones. Une approche implicite
Bibliographic record
Abstract
Dans cet article, deux approches de commande tolérante aux défauts (FTC Fault-Tolerant Control) sont étudiées et appliquées à la machine asynchrone.Dans ce contexte, la phase de détection et d'isolation du défaut est décalée par rapport à la phase de reconfiguration de la commande.Celle-ci est réalisée en testant l'état d'un modèle interne qui s'active automatiquement dès l'apparition d'un défaut pour compenser son effet.Cet effet peut être convenablement modélisé par un signal exogène issu d'un système autonome stable appelé exosystème.Une commande additive est ainsi ajoutée à la commande nominale.Issue du modèle interne, cette commande sert à compenser l'effet du défaut.La première approche FTC exploite un modèle interne basé sur l'équation de Sylvester qui entraîne une divergence lorsque la machine est affectée par deux défaut ou plus.La seconde approche, quant à elle, élimine le problème de divergence par un réglage adapté des matrices du système.ABSTRACT.This paper deals with the application of implicit fault-tolerant control techniques to induction motor drives using a Backstepping approach.For that purpose, the induction motor, the disturbances as well as the faults signals have been modeled.A Backstepping control strategy (nominal control) is then synthesized and applied to the induction motor drive for robust control purposes.For fault-tolerant control purposes, an additive control term is generated from an internal state model in order to compensate for the fault effects.Simulations carried-out on a 1.1-kW induction motor drive clearly show the effectiveness of the proposed approaches.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".