Adaptive reactionless motion for space manipulator when capturing an unknown tumbling target
Bibliographic record
Abstract
This paper presents a new adaptive algorithm to generate reactionless motion for a space manipulator during and after capturing an unknown tumbling target. The intended application scenario is on-orbit servicing whereby the service spacecraft/manipulator system must dock to, or get a hold of the target satellite in order to conduct the required operations. In the course of these missions, it is important to maintain the base attitude of the servicer unchanged. However, the changes in the dynamics parameters of the system, as a result of capturing an unknown target, degrade the performance of the attitude stabilization system. To overcome this problem in the post capture scenario, the adaptive reactionless control algorithm to produce the arm motions with minimum disturbance to the base, without knowledge of target dynamics, is proposed in this study. This algorithm is intended for use in the transition phase from the instant of capture till the unknown parameters are identified and/or the available stabilization methods can be applied properly. The proposed approach is developed based on the momentum conservation of the system, while recursive least squares algorithm is employed for parameter adaptation. To verify the validity and feasibility of the proposed concept, MSC Adams simulation platform is employed to implement a planar base-manipulator-target model. Two basic scenarios are considered: one where the initial (prior to capture) angular momentum of the target is zero and the second where the target is spinning. The numerical results show that the space manipulator is able to perform reactionless motion after capturing an unknown spinning target.
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.
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) | 0.001 | 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 | 0.000 |
| 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".