Adaptive Reactionless Motion and Parameter Identification in Postcapture of Space Debris
Bibliographic record
Abstract
This paper presents a new control scheme for the problem of a space manipulator after capturing an unknown target, such as space debris. The changes in the dynamics parameters of the system, as a result of capturing an unknown target, must be accommodated because they may lead to poor performance of the trajectory control and attitude stabilization system. To address this issue in the postcapture scenario, the adaptive reactionless control algorithm to produce the arm motions with minimum disturbance to the base is proposed in this study. In addition, the online momentum-based estimation method is developed for inertia-parameter identification after the space manipulator grasps an unknown tumbling target with unknown angular momentum. This control scheme is intended for use in the transition phase from the instant of capture until the unknown parameters are identified and/or the available stabilization methods can be applied properly. To verify the validity and feasibility of the proposed concept, MSC.Adams simulation platform is employed to implement a planar base–manipulator–target model and the three-dimensional model of the Engineering Test Satellite VII system. The numerical results show that the space manipulator is able to perform reactionless motion while the inertial parameters converge to their real values.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".