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Record W2978605562 · doi:10.1109/tmech.2019.2944387

Concurrent Proximity Control of Servicing Spacecraft With an Uncontrolled Target

2019· article· en· W2978605562 on OpenAlexaff
Qinglei Hu, Wei Chen, Youmin Zhang

Bibliographic record

VenueIEEE/ASME Transactions on Mechatronics · 2019
Typearticle
Languageen
FieldEngineering
TopicSpace Satellite Systems and Control
Canadian institutionsConcordia University
Fundersnot available
KeywordsSpacecraftControl theory (sociology)RendezvousSettling timeComputer scienceLyapunov functionLyapunov stabilityControl engineeringEngineeringControl (management)PhysicsAerospace engineeringNonlinear systemArtificial intelligence

Abstract

fetched live from OpenAlex

This paper investigates the six-degrees-of-freedom (6-DOF) rendezvous and docking issue with an uncontrolled target for a servicing spacecraft under thruster misalignment and external disturbances. Initially, a 6-DOF relative motion dynamic model is established to depict the relative position-attitude between the servicing spacecraft and uncontrolled object (freely tumbling spacecraft). Then, an adaptive fixed-time control scheme with a novel sliding manifold is employed to accomplish the 6-DOF rendezvous (close-range) and docking mission for the servicing spacecraft. Unlike existing methods, the sliding-mode surface is designed by using the new sufficient condition to achieve the system's fixed-time convergence, and this condition reduces the conservativeness of the traditional ones with less dominated terms. Moreover, the settling time of relative translation-rotation tracking errors is guaranteed irrespective of initial motion conditions. Accordingly, the fixed-time stability of the closed-loop system is guaranteed by the rigorous Lyapunov theory analysis. Finally, numerical simulation results with various cases are implemented to verify the superiority of the presented sliding-mode control strategy.

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 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 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 score0.795
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.004
GPT teacher head0.188
Teacher spread0.183 · 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 teacher head, not a consensus.

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

Citations27
Published2019
Admission routes1
Has abstractyes

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