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Record W7029766494

Longitudinal Flight Control Methodologies for Commercial Aircraft with Handling Quality Requirements

2022· other· fr· W7029766494 on OpenAlexaboutno aff

Bibliographic record

VenuePolyPublie (École Polytechnique de Montréal) · 2022
Typeother
Languagefr
FieldComputer Science
TopicNetwork Packet Processing and Optimization
Canadian institutionsnot available
Fundersnot available
KeywordsPilotageQuality (philosophy)LimitingReliability (semiconductor)
DOInot available

Abstract

fetched live from OpenAlex

RÉSUMÉ: L'introduction des technologies Fly-By-Wire dans l'aviation commerciale a permis aux avionneurs d'améliorer le comportement de leurs aéronefs par l'ajout de lois de pilotage. Néanmoins, le réglage de telles lois est difficile étant donné la grande variation du comportement d'un avion au sein de son enveloppe de vol. La solution industrielle à ce problème a été de séquencer les gains des correcteurs en fonction de paramètres facilement mesurables, telle la pression dynamique. Bien que ce processus soit simple et éprouvé, son application demande un temps important et occasionne donc des coûts significatifs. Dans ce mémoire, deux approches sont considérées pour pallier aux problèmes associés au séquencement de gains. Premièrement, la synthèse H∞ structurée est utilisée pour réduire le temps nécessaire à l'obtention d'un correcteur séquencé. Cette méthode d'optimisation robuste synthétise un correcteur rencontrant les contraintes prescrites avec une architecture prédéfinie. La méthodologie conçue permet de traduire des spécifications de performance ou de stabilité en contraintes compatibles avec la synthèse H∞ en présence d'incertitudes importantes, par exemple, l'absence de mesure de masse et de centrage. La deuxième approche vise à définir des lois de pilotage par équations (ou G*), permettant d'éviter le séquencement ainsi que de limiter les effets d'une modification du système sur le réglage des gains. Les dynamiques principales de l'avion sont placées grâce à des approximations d'ordre réduit et d'un modèle de l'avion simplifié, de sorte à obtenir un comportement satisfaisant. Cette modélisation simplifiée de l'avion, approximant l'aéronef adéquatement dans la majorité de son enveloppe, est obtenue depuis des hypothèses simplificatrices (ex. comportement linéaire). Puisque les avions modernes comportent plus de non linéarités qu'auparavant, étant donné leurs profils aérodynamiques hautement optimisés pour la croisière, ces hypothèses ne sont pas valides dans l'intégralité de l'enveloppe de vol. L'ajout de lois non linéaires permettra de s'assurer que l'avion se conforme au modèle simplifié, garantissant une bonne performance non linéaire. Finalement, les deux approches sont appliquées et comparées sur un modèle avion fourni par Airbus Canada. Cette validation permet de s'assurer de la conformité des lois conçues aux requis de designs utilisés, principalement issus de la littérature. ABSTRACT: The introduction of Fly-By-Wire technologies in commercial aviation has allowed manufacturers to augment modern aircraft with flight control laws, improving aircraft handling and pilot satisfaction. Designing such control laws is challenging due to the large changes aircraft dynamics undergo throughout the flight envelope. Manufacturers have dealt with this prob-lem through the definition of controller gains in function of easily measurable parameters (e.g. dynamic pressure), a process called gain scheduling. Although conceptually simple and extensively proven, this process is time-consuming and costly. This thesis explores two approaches to avoid common gain scheduling problems. First, long design times are addressed by numerical optimization through structured H∞ synthesis. This robust optimization method tunes a controller to meet design constraints for a pre-defined controller architecture. The conceived methodology enables the designer to translate design objectives into constraints compatible with the H∞ framework, even in the presence of large uncertainties such as the absence of information on the aircraft's mass and centre of gravity. The second approach addresses multiple weaknesses of gain scheduling approaches, such as the design time and the need to update gains after aircraft model changes (e.g. in reaction to flight tests). The control law is defined from a simplified model, which is a good approxi-mation of the aircraft in a large portion of its envelope. Doing so results in a control law "by equations" (G*), where primary aircraft dynamics are placed according to simple low-order approximations, avoiding scheduling whilst keeping the possibility to fine-tune the aircraft as desired. Practical longitudinal low-order approximations of the aircraft are developed for this methodology. As modern wing profiles are highly optimized, they are generally prone to strong nonlinearities, limiting the validity of the simplified model used. This is why a nonlinear control law is added to classical linear gains to ensure the aircraft behaves according to the simplified model, ensuring satisfactory dynamics. Finally, both approaches are tested and compared on an aircraft model provided by Airbus Canada. The conformity of both approaches to design requirements from literature will be demonstrated on this model.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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.

Opus teacher head0.065
GPT teacher head0.319
Teacher spread0.254 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreMethods

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".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

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