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Record W3032587223 · doi:10.31181/rme200101010p

Model-based fuzzy control results for networked control systems

2020· article· en· W3032587223 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

VenueReports in Mechanical Engineering · 2020
Typearticle
Languageen
FieldComputer Science
TopicFuzzy Logic and Control Systems
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsControl theory (sociology)Fuzzy control systemFuzzy logicComputer scienceControl systemStability (learning theory)Control engineeringNetworked control systemController (irrigation)Variable (mathematics)MathematicsControl (management)EngineeringArtificial intelligence

Abstract

fetched live from OpenAlex

This paper discusses aspects concerning the design of model-based fuzzy controllers for Networked Control Systems (NCSs). The stability analysis is related to the characteristic equation of these control systems, where the variable time delays create numerical problems. These numerical problems are first briefly investigated, along with signal processing aspects concerning NCSs. The popular Hilbert-Huang transform is applied to smooth the signals and also the variable time delay, also called latency, due to the communication in the network. The design of Takagi-Sugeno-Kang Proportional-Integral-fuzzy controllers dedicated to temperature control applications is next carried out; the stability of fuzzy NCSs is guaranteed by computing the controller tuning parameters as solutions to linear matrix inequalities. Experimental results for a laboratory equipment that models a first-order plus time delay process are included to validate the theoretical findings.

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)0.001
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: Methods · Consensus signal: none
Teacher disagreement score0.987
Threshold uncertainty score0.990

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.018
GPT teacher head0.210
Teacher spread0.192 · 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