Parameter-adaptive estimation and control for a single-link flexible space manipulator carrying a large uncertain payload
Bibliographic record
Abstract
End-effector control of flexible space manipulators is particularly difficult when dealing with large payloads of unknown mass. This is a relevant problem in active debris removal missions which involve grasping non-cooperative targets. This paper introduces a simultaneous parameter and state estimation algorithm that addresses the challenge of handling uncertainties from both the elastic deformation of the manipulator links as well as the unknown payload mass properties. The payload parameters are estimated using an indirect model reference adaptive control scheme, and the elastic coordinates are estimated using a Kalman filter, as well as a quasi-static closed-form estimation formula. Both the parameter and state estimates are used to improve controller performance - the estimated elastic coordinates are used to construct a modified control output called the $\mu$-tip rate which is used for task-space feedback control, while the estimated payload parameters are used to improve both the feedforward control as well as the model used in the state estimator. It is shown that the algorithm is able to converge to an accurate estimate of the payload mass and successfully stabilize the elastic deflections at the tip.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".