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Record W2395456338 · doi:10.1504/ijspacese.2015.072340

Dynamic control of space tether deployment

2015· article· en· W2395456338 on OpenAlexafffund
Zheng Zhu, Latheepan Murugathasan

Bibliographic record

VenueInternational Journal of Space Science and Engineering · 2015
Typearticle
Languageen
FieldEngineering
TopicSpace Satellite Systems and Control
Canadian institutionsYork University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPassivitySpacecraftSoftware deploymentRobustness (evolution)Control theory (sociology)Nonlinear systemOvershoot (microwave communication)Computer scienceEngineeringControl (management)PhysicsAerospace engineering

Abstract

fetched live from OpenAlex

This paper investigates the dynamic control of space tether deployment by: 1) optimal tuning of control gains of a simple tension law; 2) a passivity based tension law. The tethered spacecraft system is modelled with a nonlinear 'dumbbell' model. The tuned simple tension control law has the advantage of achieving fast tether deployment while keeping the simplicity of control law design. The passivity based control law has no overshoot of tether length and can reduce maximum in-plane pitch angle significantly. The simulation results demonstrate the efficiency and robustness of the proposed control methods.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.406
Threshold uncertainty score0.333

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.006
GPT teacher head0.217
Teacher spread0.211 · 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 teacher head, 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

Citations6
Published2015
Admission routes2
Has abstractyes

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