Formation control of high-altitude balloons experiencing real wind currents by discrete-time distributed extremum seeking control
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".