Optimisation et échelonnement des gains d'un contrôleur d'avion en tangage en utilisant les algorithmes génétiques
Bibliographic record
Abstract
Les travaux présentés dans ce rapport de maItrise concement la commande d'un avion en utilisant les algorithmes génétiques. Le système considéré est un modèle linéaire longitudinal du Challenger Learjet 604, un avion de transport commercial de chez Bombardier aéronautique. \n \nL'objectif de ce travail est de proposer une nouvelle approche pour la planification des gains d'un contrôleur existant, basée sur une modélisation mathématique. L'ensemble du travail vise á fournir une méthode globale á laquelle on peut se référer pour résoudre le problème d'ordonnancement des gains. L'idée principale est de ramener ce probléme d'ordonnancement à un problème d'optimisation. Une nouvelle formulation de la fonction objective, basée sur la robustesse de l'algorithme génétique, est alors proposée. \n \nLes résultats de simulations obtenus sur l'exemple concret du modèle longitudinal de chez Bombardier démontrent l'efficacité de l'approche proposée. \n \n
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".