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Record W3186524155 · doi:10.2514/1.a34881

Adaptive Control of a Tendon-Driven Manipulator for Capturing Non-Cooperative Space Targets

2021· article· en· W3186524155 on OpenAlexaff
Justin Kernot, Steve Ulrich

Bibliographic record

VenueJournal of Spacecraft and Rockets · 2021
Typearticle
Languageen
FieldEngineering
TopicSpace Satellite Systems and Control
Canadian institutionsCarleton University
Fundersnot available
KeywordsControl theory (sociology)Controller (irrigation)TrajectoryTransmission (telecommunications)Mechanism (biology)Computer scienceNonlinear systemControl engineeringEngineeringSimulationControl (management)PhysicsArtificial intelligence

Abstract

fetched live from OpenAlex

Orbital debris in Earth orbit poses a threat to the future of spaceflight. To combat this issue, this paper proposes a novel robotic mechanism for non-cooperative capture and active servicing missions on non-cooperative targets; specifically, a tendon-driven manipulator is assumed for this work. The capture mechanism is a prototype symmetric two-link gripper driven by an open-ended cable-sheath transmission mechanism. Because the cable-sheath transmission mechanism is a nonlinear time-varying hysteretic system, two separate adaptive control strategies were compared against the uncontrolled and proportional-integral-derivative controlled performance of the closed-loop gripper. Specifically, an indirect control method and a direct controller were employed. Experimental results demonstrate that the adaptive controllers show better tracking performance of a joint trajectory over the proportional-integral-derivative controlled and uncontrolled cases, whereas the 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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.007
GPT teacher head0.199
Teacher spread0.192 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations7
Published2021
Admission routes1
Has abstractyes

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