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Record W2108139825 · doi:10.1109/icca.2003.1595011

Robust Flight Control Design with Handling Qualities Incorporation via Low Order Model Matching

2003· article· en· W2108139825 on OpenAlexafffund
K. Hentabli, Lahcen Saydy, Ouassima Akhrif

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicControl Systems and Identification
Canadian institutionsPolytechnique MontréalÉcole de Technologie Supérieure
FundersNatural Sciences and Engineering Research Council of CanadaBombardierFlorida State University
KeywordsParametric statisticsLinear fractional transformationCenter of gravityRobust controlActuatorAerospaceController (irrigation)Control theory (sociology)Parametric modelMatching (statistics)Computer scienceEngineeringControl engineeringControl systemControl (management)Aerospace engineeringMathematicsArtificial intelligence

Abstract

fetched live from OpenAlex

In this paper, a robust pitch rate control design for longitudinal aircraft motion based on μ-synthesis is presented. A Linear Fractional Transformation (LFT) uncertainty model is first derived for robust synthesis. The uncertainty dealt with is of parametric nature and represents the variations of weight and center of gravity in the aircraft models. A controller is then developed and tested on the full longitudinal model of the Challenger 604 aircraft provided by Bombardier Aerospace Inc. The model includes both short-period and phugoïd modes of the aircraft, the stick, the actuator and the sensors. It is shown that the controller guarantees desired performance with predicted Level 1 handling qualities under varying weight and center of gravity locations.

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)0.002
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: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.020
GPT teacher head0.183
Teacher spread0.162 · 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
GenreEmpirical

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

Citations1
Published2003
Admission routes2
Has abstractyes

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