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Record W2925221731 · doi:10.1177/0142331219832945

Robust finite–time stochastic stabilization and fault–tolerant control for uncertain networked control systems considering random delays and probabilistic actuator faults

2019· article· en· W2925221731 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

VenueTransactions of the Institute of Measurement and Control · 2019
Typearticle
Languageen
FieldEngineering
TopicStability and Control of Uncertain Systems
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsControl theory (sociology)Probabilistic logicActuatorBenchmark (surveying)Computer scienceNetwork packetStability (learning theory)Controller (irrigation)Fault (geology)Control (management)

Abstract

fetched live from OpenAlex

This paper focuses on the problem of reliable finite–time stochastic stability (FTSS) for uncertain networked control systems (NCSs). A Markovian jump system (MJS) model with partly unknown transition probabilities (TPs) for the NCSs with random delays, data packet dropouts (disorders as well) and stochastic actuator faults is established to describe the closed–loop system. A mode-dependent static output feedback controller is designed taking only the measured outputs into account. A new criterion is also derived in terms of linear matrix inequalities (LMIs) to ensure reliable FTSS of the closed–loop system, based on the stochastic stability theory. Simulation studies on a benchmark numerical example, as well as an unstable numerical example can verify the effectiveness of the proposed method.

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 categoriesMeta-epidemiology (narrow)
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 score0.807
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.023
GPT teacher head0.190
Teacher spread0.167 · 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