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Record W2325122603 · doi:10.2514/6.2014-1467

Quarter Cycle Modulation of a Minimally Actuated Biomimetic Vehicle

2014· article· en· W2325122603 on OpenAlexaff
Michael W. Oppenheimer, Isaac E. Weintraub, David Sigthorsson, David Doman

Bibliographic record

VenueAIAA Guidance, Navigation, and Control Conference · 2014
Typearticle
Languageen
FieldEngineering
TopicBiomimetic flight and propulsion mechanisms
Canadian institutionsGeneral Dynamics (Canada)
Fundersnot available
KeywordsControl theory (sociology)WingAerodynamicsMicro air vehicleDegrees of freedom (physics and chemistry)Aerodynamic forceEngineeringControl systemMotion controlFlappingPhysicsComputer scienceStructural engineeringControl (management)Aerospace engineeringArtificial intelligence

Abstract

fetched live from OpenAlex

This paper describes a technique, called quarter cycle constant-period frequency modulation, to control the motion of wings on a flapping wing micro air vehicle. This technique allows control over the wingbeat period and three additional points within a single wingbeat cycle, allowing modulation of the wing’s velocity to provide control over multiple degrees-of-freedom of the vehicle. Using a blade element based aerodynamic model, both instantaneous and cycle averaged forces and moments are analytically computed for a specific type of wing beat motion that enables nearly decoupled, multiple degrees-of-freedom control of the aircraft. The wing positions are controlled using oscillators whose frequencies change once per wing beat cycle. A control oriented dynamic model of the vehicle is derived, which is based on a cycle averaged representation of the forces and moments. Control derivatives are calculated and a cycle-averaged control law is designed that provides control over multiple degrees-of-freedom of the vehicle.

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: none
Teacher disagreement score0.001
Threshold uncertainty score0.002

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 head0.005
GPT teacher head0.191
Teacher spread0.186 · 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

Citations1
Published2014
Admission routes1
Has abstractyes

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