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Record W2106463096 · doi:10.1109/acc.2006.1657635

Robust fault diagnosis for a satellite large angle attitude system using an iterative neuron PID (INPID) observer

2006· article· en· W2106463096 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicFault Detection and Control Systems
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsControl theory (sociology)Observer (physics)Convergence (economics)Computer sciencePID controllerFault detection and isolationFault (geology)Stability (learning theory)Robustness (evolution)Scheme (mathematics)State observerAlgorithmControl engineeringMathematicsArtificial intelligenceEngineeringControl (management)Nonlinear system

Abstract

fetched live from OpenAlex

A fault detection and diagnosis (FDD) scheme using an iterative neuron PID (INPID) observer is explored in this paper. The observer input, which is used to estimate state faults, is computed by utilizing the proportional, integral, and derivative information of the fault estimation error. Two classes of robust adaptive algorithms are adopted to update the parameters of the observer input. Theoretically, the convergence properties of these adaptive algorithms are investigated in two different ways, and the stability of this fault detection and diagnosis scheme is analyzed as well. Finally, the proposed FDD scheme is applied to a satellite with large angle attitude maneuvers, and the simulation results demonstrate its good performance.

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.

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: Empirical
Teacher disagreement score0.132
Threshold uncertainty score0.885

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 head0.033
GPT teacher head0.236
Teacher spread0.204 · 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

Quick stats

Citations35
Published2006
Admission routes1
Has abstractyes

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