Tip and vibration control of space robots using estimated flexible coordinates
Bibliographic record
Abstract
This paper provides an extension to previous work on end-effector control of flexible space manipulators. Those works considered the use of a special output called the μ -tip rate for feedback control of desired end-effector trajectories with simultaneous vibration control. Implementation of this special output requires measurement of end-effector position or the use of flexible forward kinematics to determine it. For the latter, one requires measurements of the joint angles and flexible coordinates. The second of these is difficult to measure in space scenarios, so this paper looks at the use of an estimation scheme to approximate it and use it in a task-space control law. Multiple simulations are conducted to investigate the use of these approximated elastic coordinates in robustly controlling a one-link and two-link flexible manipulator with a payload mass. The error between desired and actual trajectory is calculated, and the results are juxtaposed with results from a joint-space feedback scheme. There is an emphasis on comparing the estimated elastic coordinates with the actual simulated coordinates. Using the estimated elastic coordinates to determine the end-effector location via forward kinematics, yielded similar results to when the actual elastic coordinates were used. Overall, the estimation equation used is shown to provide reasonable end-effector tracking results with the end-effector being able to track various types of trajectories.
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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Open science | 0.000 | 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".