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Record W4296715334 · doi:10.18280/mmep.090424

H-Infinity Artificial Bee Colony to Dampen Sideslip and Bankroll Disturbances of Boeing 747-100 Lateral Movement

2022· article· en· W4296715334 on OpenAlexvenueno aff
Ezzeddin M. Elarbi, Saad B. Issa

Bibliographic record

VenueMathematical Modelling and Engineering Problems · 2022
Typearticle
Languageen
FieldEngineering
TopicVehicle Dynamics and Control Systems
Canadian institutionsnot available
FundersCoventry University
KeywordsMovement (music)Control theory (sociology)AeronauticsArtificial intelligenceComputer scienceEngineeringPhysicsAcousticsControl (management)

Abstract

fetched live from OpenAlex

The H-infinity method is used to augment the lateral stability of Boeing 747-100 flight at Mach numbers and altitudes of (0.2, sea-level), (0.5, 6096 m), and (0.9, 12192 m). The aim is to attenuate the lateral-directional states' perturbations coupling with the aileron and rudder. The method is synthesized with the artificial bee colony algorithm to ensure a robust quadratic performance under moderate sideslip and bankroll disturbances based on the mixed H-infinity sensitivity criteria. Such an optimizer effectively weighs the design gain matrices for at least a degree of freedom higher than without it. Stable eigenvalues and steady-state responses are reached for the step states. The controller appropriately tracks reference side velocity, roll rate and yaw rate and effectively compensates for sideslip and bankroll disturbances. Despite the transient peaks for the roll and yaw rates, level convergences are obtained for the other states. Dutch roll mode meets flying qualities of airworthiness requirements, whereas roll and spiral modes slightly diverge nearby the landing conditions. The H-infinity and artificial bee colony synthesis were well performed for bankroll and sideslip references of small to moderate perturbations. A high-fidelity optimizer may be considered for a severe level of disturbance and transitory behaviour.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.210
Threshold uncertainty score0.736

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 integrity0.0000.000
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 head0.014
GPT teacher head0.187
Teacher spread0.173 · 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 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
Published2022
Admission routes1
Has abstractyes

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