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Record W1907585848 · doi:10.5589/q10-008

Robust thrust-only control of a civil transport aircraft with vertical tail damage

2010· article· en· W1907585848 on OpenAlexaffvenue
Yoshitsugu Hitachi, Hugh H. T. Liu

Bibliographic record

VenueCanadian aeronautics and space journal · 2010
Typearticle
Languageen
FieldEngineering
TopicGuidance and Control Systems
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsFlight control surfacesThrustLinear-quadratic-Gaussian controlPropulsionAerodynamicsEngineeringRobustness (evolution)Thrust vectoringControl theory (sociology)Aerospace engineeringStructural engineeringComputer scienceControl (management)

Abstract

fetched live from OpenAlex

Thrust-only control of a jet transportation aircraft is also referred to as propulsion-controlled aircraft (PCA). It may be adopted as an alternative to using propulsive force to provide a certain level of control capability in case of failure of the aircraft's conventional control system. Previous PCA research was mainly concerned with control surface malfunctions such as free-floating and locked-in-place situations. On the other hand, structural damage to the aircraft control surfaces makes it necessary for PCA study and results in significant challenges due to the damage-induced aerodynamic and geometric parameter deviations. This paper presents a damage-tolerant control system design for aircraft with vertical tail damage. An H∞ loop transfer recovery (LTR) technique is applied to address the stability recovery and robustness of performance in maintaining safe flight operation of a damaged aircraft. Modelling of the damaged flight dynamics and control design is presented followed by numerical examination of a Boeing 747 aircraft model. The effectiveness of the proposed control approach is validated in comparison with a standard linear quadratic Gaussian (LQG) design through numerical simulations.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.529
Threshold uncertainty score0.527

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.006
GPT teacher head0.163
Teacher spread0.157 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations3
Published2010
Admission routes2
Has abstractyes

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