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Robust Control of Spacecraft Formation Flying

2007· article· en· W2102399877 on OpenAlexaff
Yan‐Ru Hu, Alfred Ng

Bibliographic record

VenueJournal of Aerospace Engineering · 2007
Typearticle
Languageen
FieldEngineering
TopicSpacecraft Dynamics and Control
Canadian institutionsCanadian Space Agency
Fundersnot available
KeywordsControl theory (sociology)SpacecraftRobustness (evolution)Robust controlLyapunov functionLyapunov stabilityController (irrigation)Bounded functionComputer scienceRelative motionControl systemControl engineeringEngineeringMathematicsControl (management)PhysicsNonlinear systemAerospace engineering

Abstract

fetched live from OpenAlex

In this paper, a robust control scheme for two spacecraft in formation subjected to time-variant external disturbances in the space environment was developed. The proposed controller consists of two parts, the first part is for the nominal system without disturbances and the second part is to compensate for effects of system disturbances. A dynamic relative motion error model was established to design the second part of the controller and to analyze the stability of the closed-loop system using Lyapunov stability theory. Furthermore, the robustness of proposed control method to the system disturbances is analyzed based on robust control theory. It is proven that the relative motion error of two spacecraft in formation is uniformly ultimately bounded under the proposed controller for the assumed disturbances in the dynamic model.

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)0.001
metaresearch head score (Gemma)0.001
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: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
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 head0.005
GPT teacher head0.171
Teacher spread0.165 · 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
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

Citations18
Published2007
Admission routes1
Has abstractyes

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