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Nonlinear Generalized Predictive Control for Earth-Orbiting Formation-Flying Spacecraft

2022· article· en· W4294691775 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

Venue2022 American Control Conference (ACC) · 2022
Typearticle
Languageen
FieldEngineering
TopicSpacecraft Dynamics and Control
Canadian institutionsCarleton University
Fundersnot available
KeywordsSpacecraftNonlinear systemControl theory (sociology)Context (archaeology)Model predictive controlController (irrigation)Optimal controlSatelliteComputer scienceAerospace engineeringNonlinear controlAttitude controlControl engineeringControl (management)EngineeringPhysicsMathematicsGeologyMathematical optimizationArtificial intelligence

Abstract

fetched live from OpenAlex

To date, most of the small satellite formation flying missions have flown with Hill-Clohessy-Wiltshire equations based controllers. Additionally, the vast majority of nonlinear optimal control methods are complex for onboard implementation with the critical need of verification and validation methods. In this context, this paper addresses the relative motion control of formation flying mission with the design and development of an analytical, onboard-compatible optimal controller based upon the Nonlinear Generalized Predictive Control theory. The resulting controller is shown to be suitable to handle perturbed near-circular orbits.

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)0.001
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.930
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.214
Teacher spread0.206 · 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