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Record W2095240134 · doi:10.1109/icuas.2013.6564774

Physical input modelling and identification for a helicopter UAV

2013· article· en· W2095240134 on OpenAlex
Bryan Godbolt, Alan F. Lynch

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdaptive Control of Nonlinear Systems
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsFlappingRotor (electric)ServomotorIdentification (biology)AerodynamicsPitch controlComputer scienceControl theory (sociology)Control engineeringEngineeringControl (management)Aerospace engineeringArtificial intelligenceMechanical engineering

Abstract

fetched live from OpenAlex

Approaches to helicopter UAV control often use the main and tail rotor thrusts, and the main rotor flapping angles as inputs. However, servomotors control the helicopter's four physical inputs which are the main rotor cyclic and collective pitch, and the tail rotor collective pitch. Helicopter models which treat the physical input are generally intractable for model-based control. We propose and identify a physical input model which is algebraically simple and therefore suitable for use in control design. The proposed model includes the vehicle's velocity to improve its accuracy.

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.

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: none
Teacher disagreement score0.728
Threshold uncertainty score0.230

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 head0.018
GPT teacher head0.223
Teacher spread0.206 · 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

Quick stats

Citations3
Published2013
Admission routes1
Has abstractyes

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