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Record W2735599454 · doi:10.23919/acc.2017.7963082

Formation control of high-altitude balloons experiencing real wind currents by discrete-time distributed extremum seeking control

2017· article· en· W2735599454 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
FieldEngineering
TopicExtremum Seeking Control Systems
Canadian institutionsQueen's University
Fundersnot available
KeywordsDelaunay triangulationVoronoi diagramController (irrigation)ComputationComputer scienceControl theory (sociology)Float (project management)Wind speedSimulationAerospace engineeringControl (management)MeteorologyEngineeringMarine engineeringAlgorithmMathematicsGeographyArtificial intelligence

Abstract

fetched live from OpenAlex

In this paper, a discrete-time method for the formation of high-altitude balloons is developed. The balloons float passively along the Earth's wind currents. For actuation, a balloon can change its altitude to enter a different wind current and move in a different direction. The control objective is to steer a fleet of balloons into a configuration where they are evenly distributed around the Earth. The control approach is a discrete-time distributed extremum-seeking controller. This controller works to minimize a measured cost function. It only requires a measurement of this cost function and does not require a model of the nonlinear time-varying wind currents. For the problem of balloon formation control, the cost function is based on a Voronoi partition resulting in an algorithm similar to Lloyd's algorithm. The control architecture is fully distributed. There is no central coordinator and each balloon receives all the information it needs by communicating to nearby balloons over a network whose structure is the Delaunay triangulation with the balloons as vertices. The resulting distributed control algorithm is computationally efficient as the burden of computation is shared between all of the balloons. Several simulations involving 1200 balloons are used to verify the effectiveness of this approach. The simulations use realistic nonlinear time-varying models which are obtained by interpolating gridded weather data obtained from the National Oceanic and Atmospheric Administration.

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 categoriesMeta-epidemiology (narrow)
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 score0.981
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.007
GPT teacher head0.220
Teacher spread0.213 · 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

Citations2
Published2017
Admission routes1
Has abstractyes

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