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Record W2113726560 · doi:10.1109/ccece.2007.47

Attitude Control of Miniature Spacecraft Using MEMS Actuators

2007· article· en· W2113726560 on OpenAlexafffund
Yong-Lin Kuo, Krishna Dev Kumar, Kamran Behdinan, Zouheir Fawaz

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicStructural Analysis and Optimization
Canadian institutionsToronto Metropolitan University
FundersCanada Research Chairs
KeywordsSpacecraftActuatorKinematicsAttitude controlControl theory (sociology)Rotation (mathematics)Reaction wheelAngular velocityMicroelectromechanical systemsPhysicsAngular momentumTorqueRotation around a fixed axisInverse kinematicsComputer scienceAerospace engineeringEngineeringClassical mechanicsControl (management)

Abstract

fetched live from OpenAlex

The paper examines a novel attitude control method based on MEMS actuators. Each actuator (called pseudo-wheel) comprises of four small beams of electrothermal materials. If these beams undergo the transverse deformation in a clockwise rotation, the spacecraft rotates in counter-clock-wise direction in order to conserve the system angular momentum. However, when the actuator is off, the beams come to their undeformed configurations because of their elastic properties. This problem can be overcome by employing a sequence of large rotations caused by deflecting actuators about two or three orthogonal axes to produce a net attitude change. This paper studies rotation sequences, kinematics analysis of spacecraft, and simulation of attitude motion. Assuming the conservation of system angular momentum and employing inverse kinematics, the time response of attitude motion of the spacecraft is simulated. This approach not only just searches a possible solution based on an open-loop control strategy, but also determines a set of optimal rotating rates of MEMS actuators, which can produce a minimum kinetic energy required for attitude control.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.006
GPT teacher head0.217
Teacher spread0.211 · 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 designBench or experimental
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
Published2007
Admission routes2
Has abstractyes

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