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Record W2105616725 · doi:10.1109/tfuzz.2011.2180391

Decentralized Fault-Tolerant Control for Satellite Attitude Synchronization

2011· article· en· W2105616725 on OpenAlexaff
Junquan Li, Krishna Dev Kumar

Bibliographic record

VenueIEEE Transactions on Fuzzy Systems · 2011
Typearticle
Languageen
FieldComputer Science
TopicDistributed Control Multi-Agent Systems
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsControl theory (sociology)Computer scienceActuatorFault toleranceSynchronization (alternating current)Controller (irrigation)Attitude controlFuzzy logicFault (geology)Terminal sliding modeControl engineeringSliding mode controlControl (management)Distributed computingEngineeringChannel (broadcasting)Nonlinear systemArtificial intelligence

Abstract

fetched live from OpenAlex

This paper presents a decentralized adaptive fuzzy approximation design to achieve attitude tracking control for formation flying in the presence of external disturbances and actuator faults. A nonsingular fast terminal sliding mode controller that is based on consensus theory is designed for distributed cooperative attitude synchronization. It solves synchronization issues between multiple satellites by information topology. In the proposed control scheme, a fuzzy logic system (FLS) is introduced to approximate unknown individual satellite attitude dynamics due to actuator faults. In order to achieve fault management without the involvement of ground-station operators, the proposed control laws do not require an explicit fault detection and isolation mechanism. Numerical simulation results including actuator dynamics and initial condition uncertainties show that the proposed strategy with FLS can compensate for a fault. The system continues to operate after wheel faults, and the closed-loop distributed tracking control system is stochastically stable.

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)0.000
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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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 science0.0010.000
Research integrity0.0000.000
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 head0.032
GPT teacher head0.242
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

Citations105
Published2011
Admission routes1
Has abstractyes

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