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Record W2326081963 · doi:10.2514/6.2013-4959

Passivity-Based Adaptive Attitude Control with Explicit Gravity-Gradient Torque Compensation

2013· article· en· W2326081963 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.

Bibliographic record

VenueAIAA Guidance, Navigation, and Control (GNC) Conference · 2013
Typearticle
Languageen
FieldEngineering
TopicAdaptive Control of Nonlinear Systems
Canadian institutionsMcGill UniversityUniversity of Toronto
Fundersnot available
KeywordsControl theory (sociology)Compensation (psychology)TorquePassivityAdaptive controlComputer scienceAttitude controlControl (management)Control engineeringPhysicsEngineeringArtificial intelligencePsychologySocial psychology

Abstract

fetched live from OpenAlex

Passivity-based adaptive attitude control in the presence of a gravity-gradient disturbance torque is considered. Traditional passivity-based adaptive attitude control adaptively estimates the spacecraft inertia matrix. To guarantee closed-loop stability via the passivity theorem, the system inputs (the disturbances) must be L2 functions. Unfortunately, the gravity-gradient disturbance torque is not an L2 disturbance. To this end, we propose a modification to the traditional passivitybased adaptive attitude control architecture where the gravity-gradient disturbance torque is incorporated into the control and adaptation laws directly. This is possible because the gravity-gradient torque is a linear function of the spacecraft inertia matrix. We show that our modified passivity-based adaptive attitude control method is guaranteed to stabilize the closed-loop system via the passivity theorem. A numerical example is included.

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 categoriesMeta-epidemiology (narrow)
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 score0.778
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.009
GPT teacher head0.194
Teacher spread0.185 · 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