Adaptive Control of a Tendon-Driven Manipulator for the Capture of Non-Cooperative Space Targets
Bibliographic record
Abstract
Orbital debris in Earth orbit poses a threat to the future of spaceflight. To combat this issue, the rigid capture of these non-cooperative orbital targets is enabled through the use of dexterous robotic manipulators. This paper proposes a novel robotic mechanism for capture and active servicing missions on non-cooperative targets; specifically, a tendon-driven manipulator is developed in this work. The capture mechanism is a prototype symmetric two-link gripper driven by an open-ended cable-sheath transmission mechanism. Since the cable-sheath transmission mechanism is a non-linear time varying hysteretic system, two separate adaptive control strategies were compared against the open-loop and proportional-integral-derivative controlled performance of the closed-loop gripper. Specifically, an indirect control method and a direct L1 controller are proposed. Experimental results demonstrate that the adaptive controllers show better tracking performance of a joint trajectory over the proportional-integral-derivative and uncontrolled cases, while the L1 controller performs best under dynamic conditions, and the indirect controller performs best in steady state.
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) | 0.000 | 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".