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Record W2967441303 · doi:10.1109/rose.2019.8790425

An Online Reinforcement Learning Wing-Tracking Mechanism for Flexible Wing Aircraft

2019· article· en· W2967441303 on OpenAlex

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
FieldComputer Science
TopicAdaptive Dynamic Programming Control
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsWingReinforcement learningMechanism (biology)Computer scienceAeronauticsAerospace engineeringSimulationArtificial intelligenceEngineeringPhysics

Abstract

fetched live from OpenAlex

Flexible wing aircraft are gaining an increasing interest due to their salient features, such as inexpensive market price, low-cost operation, in-flight robustness, multi-purpose use, and their ability to operate with very little infrastructure. The continuous variations in the aerodynamics of the wing and additionally the kinematic and dynamic constraints that evolve due to the wing-fuselage interactions make the modeling task of such systems ultimately challenging. An online model-free adaptive control mechanism based on two linear actuation systems is proposed in this manuscript to fulfill different pitch-roll maneuvers. The mechanism employs model-free tracking control strategies and utilizes a real-time value iteration-based reinforcement learning process. The adaptation of the control gains is accomplished online using means of adaptive critics.

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)0.001
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: Methods · Consensus signal: none
Teacher disagreement score0.924
Threshold uncertainty score0.825

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.025
GPT teacher head0.278
Teacher spread0.252 · 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

Citations5
Published2019
Admission routes1
Has abstractyes

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