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Record W2779392991 · doi:10.1109/tcst.2017.2780061

Observer-Based Output Feedback Attitude Stabilization for Spacecraft With Finite-Time Convergence

2017· article· en· W2779392991 on OpenAlexaff
Qinglei Hu, Boyan Jiang, Youmin Zhang

Bibliographic record

VenueIEEE Transactions on Control Systems Technology · 2017
Typearticle
Languageen
FieldEngineering
TopicAdaptive Control of Nonlinear Systems
Canadian institutionsConcordia University
FundersNational Natural Science Foundation of China
KeywordsControl theory (sociology)Angular velocityObserver (physics)Lyapunov functionConvergence (economics)SpacecraftMathematicsStability (learning theory)Attitude controlStability theoryController (irrigation)Computer scienceControl (management)EngineeringPhysicsControl engineeringNonlinear systemClassical mechanics

Abstract

fetched live from OpenAlex

This brief investigates the problem of finite-time output feedback control for spacecraft attitude stabilization without angular velocity measurement. First, two new sufficient conditions for finite-time ultimate boundedness and local finite-time stability are derived, which reduce the conservativeness of the traditional conditions. Then, based on the two new sufficient conditions of the finite-time stability, a finite-time observer is proposed to estimate the unknown angular velocity by using the quadratic Lyapunov function method. Next, a finite-time attitude controller is designed based on the estimate of the angular velocity. The finite-time stability of the entire closed-loop system is analyzed through the Lyapunov approach. The rigorous proof shows that the observation errors and the spacecraft attitude will converge to a residual set of zero in finite time. Numerical simulation results illustrate the effectiveness of the proposed strategy.

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)0.001
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 score0.003
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.018
GPT teacher head0.228
Teacher spread0.210 · 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

Citations142
Published2017
Admission routes1
Has abstractyes

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