MétaCan
Menu
Back to cohort
Record W1632293325

Observer-based finite time control for spacecraft attitude stabilization

2013· article· en· W1632293325 on OpenAlexaff
Bo Li, Qinglei Hu, Jiang Chengping, Danwei Wang, Youmin Zhang

Bibliographic record

VenueChinese Control Conference · 2013
Typearticle
Languageen
FieldEngineering
TopicAdaptive Control of Nonlinear Systems
Canadian institutionsConcordia University
Fundersnot available
KeywordsControl theory (sociology)SpacecraftDifferentiatorState observerAttitude controlInertiaRobustness (evolution)Observer (physics)ActuatorSliding mode controlLyapunov functionComputer scienceConvergence (economics)EngineeringControl engineeringControl (management)Bandwidth (computing)PhysicsNonlinear systemAerospace engineering
DOInot available

Abstract

fetched live from OpenAlex

A novel time-varying sliding mode control based finite-time controller incorporated a modified second-order disturbances observer/differentiator is proposed for a rigid spacecraft attitude stabilization control system. Firstly, a modified observer is designed to estimate the time-varying sliding mode manifold and the combined disturbances caused by external disturbances, spacecraft inertia uncertainties and actuator misalignments. Then based the estimated values derived from the observer, a finite-time controller and an adaptive law for the time-varying variable gain are presented, and Lyapunov stability analysis shows that finite-time convergence of the uncertain spacecraft attitude control system to the equilibrium point can be accomplished with great robustness to disturbances and uncertainties. Numerical simulation results for the orbiting rigid spacecraft model show fine performance, which validates the effectiveness and feasibility of the proposed scheme.

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 score0.005

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 science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.014
GPT teacher head0.219
Teacher spread0.206 · 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

Citations2
Published2013
Admission routes1
Has abstractyes

Explore more

Same venueChinese Control ConferenceSame topicAdaptive Control of Nonlinear SystemsFrench-language works237,207