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Record W2018246578 · doi:10.2514/6.2010-8296

Formation Flying Control of Small Satellites

2010· article· en· W2018246578 on OpenAlexaff
Junquan Li, Yaodong Pan, Krishna Dev Kumar

Bibliographic record

VenueAIAA Guidance, Navigation, and Control Conference · 2010
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstro and Planetary Science
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsAeronauticsComputer scienceAstrobiologyEngineeringPhysics

Abstract

fetched live from OpenAlex

A new second order sliding mode (2nd-SM) control algorithm of MIMO nonlinear systems for low thrust multiple picosatellites formation flying is proposed in this paper. The problem is tackled by applying chatteringfree 2nd-SM control law as a control scheme for the case where the leader satellite is in circular and elliptical reference orbits under J2 perturbation. The asymptotic convergence to the 2nd-SM and the robust stability of the asymptotic 2nd-SM control system are for the first time proved with Lyapunov functions. Several numerical results are presented to demonstrate the efficacy of the proposed controllers in maintaining the satellite formation in presence of initial formation errors and J2 perturbation effects that tend to disperse the formation. Simulation results confirm that the suggested methodologies yield high formation keeping precision and effectiveness in ensuring formation maneuvering with thrusters faults of formation flying systems for picosatellites. Using this 2nd-SM method the fuel consumption is lower than in most other works.

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: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.003
Threshold uncertainty score0.005

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 head0.008
GPT teacher head0.206
Teacher spread0.198 · 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
GenreMethods

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
Published2010
Admission routes1
Has abstractyes

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