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Record W3083547791 · doi:10.2514/1.g005006

Koopman-Operator-Based Attitude Dynamics and Control on SO(3)

2020· article· en· W3083547791 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of Guidance Control and Dynamics · 2020
Typearticle
Languageen
FieldPhysics and Astronomy
TopicModel Reduction and Neural Networks
Canadian institutionsYork University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsControl theory (sociology)Angular velocityNonlinear systemOperator (biology)Controller (irrigation)Linear systemAttitude controlSet (abstract data type)Synchronization (alternating current)MathematicsNonlinear controlComputer scienceObservableControl (management)Control engineeringEngineeringTopology (electrical circuits)Mathematical analysisPhysicsArtificial intelligenceClassical mechanics

Abstract

fetched live from OpenAlex

This paper presents an attitude control method based on Koopman operator theory. A set of observables is discovered to represent the nonlinear attitude dynamics on using an infinite-dimensional linear system. With the assumption of low angular velocities, a finite-dimensional linear system is obtained by removing the high-order terms. An attitude control synthesis method is developed on the basis of the linear optimal control algorithm for the reduced linear system. The proposed controller design method is compared with some classical nonlinear optimal controllers to show its advantages. Also, the leaderless attitude synchronization problem is solved with the help of the reduced linear system and the well-studied linear multiagent system theory. Furthermore, a possible solution to the case with a high angular velocity is provided. Simulations and experiments are conducted to verify the effectiveness of the theory.

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.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.792
Threshold uncertainty score0.623

Codex and Gemma teacher scores by category

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 head0.008
GPT teacher head0.228
Teacher spread0.220 · 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