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Solar-Drag Spacecraft Formation Control with Particle Swarm Optimization-Based Guardian Maps

2024· article· en· W4402260824 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSpacecraft Dynamics and Control
Canadian institutionsCarleton University
Fundersnot available
KeywordsSpacecraftGuardianDragParticle swarm optimizationAerospace engineeringSwarm behaviourComputer sciencePhysicsControl theory (sociology)Control (management)EngineeringArtificial intelligenceAlgorithm

Abstract

fetched live from OpenAlex

This paper presents a new control law that combines solar radiation pressure and atmospheric drag as a forms of actuation with thrusters to reduce the fuel necessary to perform formation reconfiguration and maintenance. Specifically, utilizing the novel combination of particle swarm optimization algorithm with the stabilization technique of Guardian Map theory, a control law that is robust to changes in orbital position and angle with respect to the sun and velocity vectors is proposed. A linear parameter varying model of the chaser spacecraft that considers the effects of a third-body, and the nonlinear model of solar radiation pressure and atmospheric drag is also proposed in this paper and used in the development of the control law. Results show a reduction in fuel-consumption during the transients of formation reconfiguration and maintenance.

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.

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: none
Teacher disagreement score0.992
Threshold uncertainty score0.541

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 head0.003
GPT teacher head0.165
Teacher spread0.162 · 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

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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