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Record W1996046897 · doi:10.1049/iet-cta:20050492

Synthesis of active fault-tolerant control based on Markovian jump system models

2007· article· en· W1996046897 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

VenueIET Control Theory and Applications · 2007
Typearticle
Languageen
FieldEngineering
TopicFault Detection and Control Systems
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsControl theory (sociology)Linear matrix inequalityFault toleranceMathematical optimizationController (irrigation)Markov chainMathematicsLinear systemConvex optimizationStability (learning theory)Computer scienceRegular polygonControl (management)

Abstract

fetched live from OpenAlex

In this paper active fault-tolerant control (FTC) is designed in a stochastic framework. The fault-tolerant control system (FTCS) is formulated as a set of linear systems governed by two continuous-time finite-state Markov chains, which are used to characterise the system failure modes and the fault detection and isolation (FDI) scheme. This framework is widely accepted for stability analysis of FTCS; however, the design of a controller only accessing the FDI mode is still a challenging problem. We solve this synthesis problem by using convex optimisation techniques. First, a sufficient condition for the mean exponential stability is given in terms of a linear matrix inequality (LMI). The results are then extended to uncertain systems design for stability and in system performance using a stochastic integral quadratic constraint. Due to the complexity of the problem, the controller is obtained using the iterative LMI technique.

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)0.001
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: none
Teacher disagreement score0.991
Threshold uncertainty score0.771

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.004
GPT teacher head0.198
Teacher spread0.194 · 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