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Record W2163104417 · doi:10.1109/icsmc.1995.538088

Dynamics and control of flexible multibody systems: an application to orbiting platforms

2002· article· en· W2163104417 on OpenAlexaff
Anant Grewal, V. J. Modi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicDynamics and Control of Mechanical Systems
Canadian institutionsUniversity of British ColumbiaNational Research Council Canada
Fundersnot available
KeywordsMultibody systemNonlinear systemComputer scienceControl theory (sociology)Linear-quadratic-Gaussian controlCode (set theory)Control engineeringRigid bodyDegrees of freedom (physics and chemistry)Dynamics (music)Equations of motionController (irrigation)Dynamical systems theoryControl (management)EngineeringPhysicsClassical mechanics

Abstract

fetched live from OpenAlex

The paper focuses on an approach to the study of the dynamics and control of large flexible space structures which comprise of sub-assemblies. To begin with, a relatively general Lagrangian formulation of the problem is developed. The governing equations are nonlinear, nonautonomous, coupled and extremely lengthy even in matrix notation. Next, an efficient computer code is developed and the versatility of the program illustrated through a dynamical study of the first element launch (FEL) configuration of the Space Station Freedom, now superseded by the International Space Station. Finally, robust control of the rigid body motion of the FEL configuration using both the LQG/LTR and H/sub /spl infin//, procedures is demonstrated. The controllers designed using the simplified linear models, prove to be effective in regulating librational disturbances. Such a global approach-formulation, numerical code, dynamics and control- can serve as a powerful tool to gain comprehensive understanding of dynamical interactions and thus aid in the development of an effective and efficient control system.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.828
Threshold uncertainty score0.351

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 head0.007
GPT teacher head0.201
Teacher spread0.195 · 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

Citations9
Published2002
Admission routes1
Has abstractyes

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