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Record W2071174849 · doi:10.1109/acc.2010.5530875

Transitions between level flight and hovering for a fixed-wing mini aerial vehicle

2010· article· en· W2071174849 on OpenAlexafffund
Vincent Myrand-Lapierre, André Desbiens, Éric Gagnon, Frank Wong, Éric Poulin

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdaptive Control of Nonlinear Systems
Canadian institutionsDefence Research and Development CanadaUniversité Laval
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsFixed wingControl theory (sociology)Controller (irrigation)TestbedTrajectorySupervisorComputer scienceFlight dynamicsSimulationEngineeringAerospace engineeringControl engineeringControl (management)WingAerodynamicsArtificial intelligencePhysics

Abstract

fetched live from OpenAlex

This article describes a control strategy to bridge the autonomous transition between level and hovering flight of a fixed-wing mini-aerial vehicle. These autonomous transitions, combined with the level-flight and hovering modes, would permit dangerous missions like reconnaissance in hostile or restricted areas. Then, there are four flight modes during a mission : the level-flight, the level-flight to hovering (L2H), the hovering, and the hovering to level-flight (H2L). The model structures for both level-flight and hovering modes are based on the linearization of a six-degree-of-freedom rigid body model of a fixed-wing mini-aerial vehicle. Controllers for both main flying modes are presented. The L2H mode is managed by the level-flight controller, whereas the H2L is managed by the hovering controller. A systematic approach based on a logic-based switching supervisor is developed to manage the transition between modes. Experimental results of a mini-aerial vehicle testbed which uses the switching supervisor are presented.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.027
GPT teacher head0.236
Teacher spread0.209 · 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 designBench or experimental
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

Citations15
Published2010
Admission routes2
Has abstractyes

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