Capture of Spinning Target with Space Manipulator using Magneto Rheological Damper
Bibliographic record
Abstract
uid, to produce a controllable damper. There are many successful applications of MR damper in ground-based and aviation systems. However, application of this device on a space manipulator has not been addressed yet. In this paper, we investigate the possibility of using MR damper to deal with problems occurring in post-capture scenario. In this study, a 1DOF base-dampertarget model and a 3DOF base-manipulator-target system equipped with the MR dampers at the joints are employed to verify the validity and feasibility of the proposed concept. The simulation results show that the MR damper with a feedback proportional controller can eciently dampen the angular momentum of the spinning target and bring it to rest relative to the base without knowledge of manipulator or target dynamics. Comparison of the performance of a passive damper and controllable damper (MR damper) is also presented. In addition, our analysis of the system also demonstrates that there is a maximum amount of kinetic energy which can be dissipated by the space manipulator during and/or after capture of a tumbling target and this value depends on the initial angular momentum of the target and the arm conguration at capture. The minimum kinetic energy formula is derived and veried through numerical simulation.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".