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Record W1938423749 · doi:10.1109/iros.1994.407649

Stabilizing tethered satellite systems using space manipulators

2002· article· en· W1938423749 on OpenAlexafffund
Mohammad Jafar Sadigh, Arun K. Misra

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSpace Satellite Systems and Control
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsLinearizationControl theory (sociology)Computer scienceMotion (physics)Equations of motionSatelliteFeedback linearizationSet (abstract data type)Controller (irrigation)Nonlinear systemArtificial intelligenceControl (management)PhysicsAerospace engineeringEngineering

Abstract

fetched live from OpenAlex

The use of space manipulators to stabilize a two-body tethered satellite system (TSS) during the stationkeeping and retrieval phases, is considered. The uncontrolled motion of the system in the stationkeeping phase is marginally stable, while during the retrieval phase it is unstable. The stabilization is carried out by means of a two-link space manipulator, similar to the CANADARM. The motion is considered to be planar and the joint torques are considered as inputs. The equations of motion for the system are analytically generated, using FLXSIM, a symbolic computer code based on Kane's method. For the stationkeeping phase, the equations of motion are linearized around the trim condition (fixed point) and the LQR method is used to devise a state-feedback based controller for the system. The results prove the method to be practical. For the retrieval case, with a non-autonomous set of equations, a modified feedback linearization technique is employed to alleviate the problem of not having enough inputs for the standard feedback linearization technique.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

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)0.000
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 score0.321
Threshold uncertainty score0.808

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.033
GPT teacher head0.191
Teacher spread0.159 · 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

Citations11
Published2002
Admission routes2
Has abstractyes

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