MétaCan
Menu
Back to cohort
Record W4303438484 · doi:10.2514/1.g006260

Cascaded Lyapunov Vector Fields for Acceleration-Constrained Spacecraft Path Planning

2022· article· en· W4303438484 on OpenAlexafffund
Jeffrey G. Hough, Steve Ulrich

Bibliographic record

VenueJournal of Guidance Control and Dynamics · 2022
Typearticle
Languageen
FieldEngineering
TopicAdaptive Control of Nonlinear Systems
Canadian institutionsCarleton University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsLyapunov functionAccelerationSpacecraftControl theory (sociology)Vector fieldBounded functionLyapunov stabilityComputer scienceStability (learning theory)Path (computing)Stability theoryMotion planningMathematicsPhysicsNonlinear systemEngineeringAerospace engineeringArtificial intelligenceMathematical analysisClassical mechanics

Abstract

fetched live from OpenAlex

A variant of Lyapunov vector fields is presented for tracking trajectories within tumbling and accelerating reference frames. This extension is computationally light, and is acceleration constrained rather than velocity constrained, making it suitable for real-time use in spacecraft. A general stability analysis proves globally asymptotic stability given a set of conditions that are analogous to those of standard Lyapunov vector fields. A special case of this novel path-planning law (referred to as a cascaded Lyapunov vector field) is closely studied, and a simple set of conditions guaranteeing bounded acceleration commands for perfect tracking are derived. A design procedure is presented. Finally, a full design example for a spacecraft proximity inspection mission is presented. The simulations demonstrate stable behavior while respecting acceleration and path constraints. Furthermore, all constraints are met by judicious design of the path-planning field, without the need for computationally expensive algorithms running in real-time.

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 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 score0.965
Threshold uncertainty score0.546

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 head0.011
GPT teacher head0.231
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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, 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

Citations7
Published2022
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Guidance Control and DynamicsSame topicAdaptive Control of Nonlinear SystemsFrench-language works237,207