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Control of a Passively-Coupled Hybrid Aircraft

2020· article· en· W3091993731 on OpenAlexaff
Christian Patience, Meyer Nahon

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdaptive Control of Nonlinear Systems
Canadian institutionsMcGill University
Fundersnot available
KeywordsController (irrigation)Tilt (camera)Rotor (electric)Flight testAerodynamicsDroneComputer scienceFly-by-wireFlight dynamicsAerospace engineeringAileronAircraft flight mechanicsTrajectoryFlight envelopeMATLABFixed wingVehicle dynamicsEngineeringFlight simulatorWingMechanical engineering

Abstract

fetched live from OpenAlex

Unmanned aerial vehicles have gained popularity in applications such as farming and package delivery, due to their low cost and versatility. The two traditional existing aircraft architectures are fixed-wing and rotorcraft, each with distinct advantages. Tilt-rotor hybrid aircraft blend the two architectures and retain the advantages of both. However, their complex dynamics and broader flight envelope make them more difficult to control. The Vogi UAV can be classified as a tilt-rotor aircraft and comprises a fixed-wing body with a passively-hinged quadrotor frame. In this paper, we present a single controller that seamlessly handles the transition from vertical to forward flight. The controller is evaluated in a comprehensive Matlab/Simulink flight simulation. The test trajectory used covers all flight modes that a hybrid aircraft would undergo, including vertical take off, forward flight with turns, and vertical landing. Since the proposed control strategy is largely platform-independent, it can be generalized to other tilt-rotor aircraft with modifications to the control allocation.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.943
Threshold uncertainty score0.423

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 head0.012
GPT teacher head0.193
Teacher spread0.181 · 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

Citations0
Published2020
Admission routes1
Has abstractyes

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