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Record W2728419698 · doi:10.2514/1.g002617

Control of Asteroid-Hovering Spacecraft with Disturbance Rejection Using Position-Only Measurements

2017· article· en· W2728419698 on OpenAlexaff
Haichao Gui, Anton de Ruiter

Bibliographic record

VenueJournal of Guidance Control and Dynamics · 2017
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstro and Planetary Science
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsControl theory (sociology)SpacecraftInertial frame of referenceTrajectoryComputer scienceObserver (physics)Controller (irrigation)State observerPosition (finance)Lyapunov functionEngineeringControl (management)PhysicsAerospace engineeringNonlinear systemArtificial intelligence

Abstract

fetched live from OpenAlex

This paper addresses the control of spacecraft hovering about uniformly rotating asteroids using only position measurements. The highly uncertain dynamical environments and the absence of velocity feedback make it a challenging issue. To overcome the difficulties, an extended state observer is designed to estimate the spacecraft velocity and uncertain perturbations simultaneously. It ensures ultimately bounded estimation errors, irrespective of the control law applied. A velocity-free hovering controller is then obtained by driving a full-state feedback controller with estimates from the observer. The observer and controller design accounts for both measurement errors and unknown perturbations, and a Lyapunov analysis shows that the resultant control scheme globally stabilizes the spacecraft trajectory to the vicinity of the desired hovering state. Furthermore, autonomous hovering operations, implemented by either continuous or on–off thrusters, in both the body-fixed frame and the inertial frame are simulated with sensor noise and multiple unknown disturbances to demonstrate the effectiveness of the proposed methods.

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)0.001
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 score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 science0.0000.001
Research integrity0.0000.001
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 head0.010
GPT teacher head0.229
Teacher spread0.219 · 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

Citations44
Published2017
Admission routes1
Has abstractyes

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