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Record W1663267617 · doi:10.1109/tie.2015.2432107

Fault-Tolerant Attitude Stabilization for Satellites Without Rate Sensor

2015· article· en· W1663267617 on OpenAlexaff
Bing Xiao, Mingyi Huo, Xuebo Yang, Youmin Zhang

Bibliographic record

VenueIEEE Transactions on Industrial Electronics · 2015
Typearticle
Languageen
FieldEngineering
TopicFault Detection and Control Systems
Canadian institutionsConcordia University
FundersNational Natural Science Foundation of China
KeywordsControl theory (sociology)Angular velocityAttitude controlActuatorObserver (physics)Fault toleranceController (irrigation)Computer scienceMode (computer interface)EngineeringControl engineeringControl (management)PhysicsArtificial intelligence

Abstract

fetched live from OpenAlex

A fault-tolerant control approach without rate sensors is presented for the attitude stabilization of a satellite being developed. External disturbances, reaction wheel faults, actuator saturation, and unavailable angular velocity are addressed. A sliding-mode observer is proposed by using attitude feedback only, and the unavailable angular velocity is estimated by this observer in finite time. Using the attitude and the estimated velocity, another sliding-mode observer is proposed to reconstruct actuator faults and disturbances. It is proven that reconstruction with zero observer error is achieved in finite time. With the reconstructed value, a velocity-free controller is then developed to asymptotically stabilize the attitude. Simulation results are also provided to verify the effectiveness of the proposed approach.

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 score0.002
Threshold uncertainty score0.003

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 science0.0000.000
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 head0.044
GPT teacher head0.257
Teacher spread0.213 · 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

Citations99
Published2015
Admission routes1
Has abstractyes

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