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Record W2901229630 · doi:10.25071/10315/35240

Lagrangian, Hamiltonian, And Energy-Based Control For Space Tethered System

2018· article· en· W2901229630 on OpenAlexaff
Junjie Kang, Zheng Zhu

Bibliographic record

VenueProgress in Canadian Mechanical Engineering · 2018
Typearticle
Languageen
FieldEngineering
TopicSpace Satellite Systems and Control
Canadian institutionsYork University
Fundersnot available
KeywordsLagrangianHamiltonian (control theory)Computer scienceHamiltonian systemSpace (punctuation)Control systemControl theory (sociology)Control (management)Mathematical optimizationPhysicsApplied mathematicsClassical mechanicsMathematicsEngineeringElectrical engineeringArtificial intelligence

Abstract

fetched live from OpenAlex

A typical and useful way to derive the dynamics equation of the tethered systems is by means of the Lagrange's equations and various dynamic models of different tethered missions are established by the Lagrangian formulation. The Hamiltonian formulation is also widely used in the mechanical systems for its well-known symplectic structure property. With this in mind, the dynamics equation of the tethered system's motion are deduced by Hamilton's equations in this research. The relation between the Lagrangian and Hamiltonian is shown by Legendre transformation. The goodness of the Hamiltonian formulation is intuitive to reveal the Energy balance property that corresponds to the passivity property. Furthermore, the Hamiltonian energy function of tethered system is employed for facilitating the controller design. In order to bring the system into operations, the energy based control is to achieve the tethered system for precise positioning. Simulations are used to demonstrate the effectiveness of the designed controller.

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 categoriesMeta-epidemiology (narrow)
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 score0.940
Threshold uncertainty score1.000

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 head0.005
GPT teacher head0.191
Teacher spread0.186 · 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.

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

Citations0
Published2018
Admission routes1
Has abstractyes

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