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Record W2106346145 · doi:10.1109/cdc.2009.5399684

On the attitude recovery of an underactuated spacecraft using two control moment gyroscopes

2009· article· en· W2106346145 on OpenAlexaff
Ali Reza Mehrabian, S. Tafazoli, K. Khorasani

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicControl and Dynamics of Mobile Robots
Canadian institutionsConcordia UniversityCanadian Space Agency
Fundersnot available
KeywordsControl theory (sociology)UnderactuationAttitude controlActuatorControl moment gyroscopeReaction wheelNonholonomic systemGyroscopeSpacecraftTorqueLyapunov functionExponential stabilityComputer scienceController (irrigation)Control engineeringControl systemControl (management)EngineeringMobile robotPhysicsAerospace engineeringRobotNonlinear systemArtificial intelligence

Abstract

fetched live from OpenAlex

In recent years there has been a growing interest in development and utilization of small satellites (smallsats) for scientific missions using single or multiple spacecraft (SC). Due to their small size and low mass, it is not generally possible to equip smallsats with redundant actuators. In case of a failure in the SC's attitude control actuators, one has to reconfigure the control laws in order to maintain the SC's attitude by using only the remaining healthy actuators. In this work, an algorithm that is based on nonholonomic control theory and hybrid control systems is presented for recovery of an underactuated axis-symmetric SC (having only two control torque channels). Our goal is that the recovered SC, in addition to being stabilized, can slew to a desired attitude. Using Lyapunov analysis, the asymptotic stability of the system is guaranteed by employing the proposed control law. Simulations are performed to demonstrate the performance capabilities of the recovered system.

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 score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.010
GPT teacher head0.239
Teacher spread0.229 · 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

Citations4
Published2009
Admission routes1
Has abstractyes

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