Experimental Validation of Detumbling Space Debris by Tethered Space Tug by Air Bearing Testbed
Bibliographic record
Abstract
To study the problem of removing tumbling space debris by space tether system, the current work establishes the dynamic model of a large rotating debris by an actively controlled small tethered tug in a central gravitational filed. The elastic-viscous tether is modeling as a spring damper. Meanwhile, the singularity free modified equinoctial elements of the orbit are used to describe the orbital motion and the sway dynamics of the space tethered system’s attitude motion are established in the local vertical and local horizon (LVLH) frame. A simple anti-sway control strategy, attitude control and tension control, is proposed with only using partial measurements (only position measurement). The post-capture dynamics of space debris towed by a tethered space tug and de-tumbling control are examined and verified experimentally on the ground air bearing testbed which can mimic the micro gravity environment. The testbed is a 2m x 4m granite table. Two air-bearing platforms that can move freely on the table are used in the experiment. One simulates the free floating space debris and another simulates the space tug. They are connected by a tether. The tug’s position and attitude are actively controlled in the experiment. The position and attitude of the debris and tug are measured by the star trackers and optical gyros onboard the two free floating test platforms. The testing results agree with the computer simulation very well.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".