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Record W2611328011 · doi:10.1002/oca.2326

Secondary delay‐partition approach on robust performance analysis for uncertain time‐varying Lurie nonlinear control system

2017· article· en· W2611328011 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

VenueOptimal Control Applications and Methods · 2017
Typearticle
Languageen
FieldEngineering
TopicStability and Control of Uncertain Systems
Canadian institutionsUniversity of Waterloo
FundersNational Natural Science Foundation of China
KeywordsControl theory (sociology)Partition (number theory)Nonlinear systemInterval (graph theory)Robust controlStability (learning theory)MathematicsComputer scienceControl (management)Artificial intelligence

Abstract

fetched live from OpenAlex

Summary This paper investigates the problem of robust performance analysis for Lurie nonlinear control system with parameter uncertainties and interval time‐varying delays. Based on an augmented Lyapunov‐Krasovskii functional including multiple integral terms, new delay‐dependent robust stability criteria are derived by proposing a novel secondary delay‐partition approach. Moreover, to obtain less conservative stability conditions, an optimized integral inequality is developed by introducing an adjustable parameter ς 1 . Finally, 5 numerical simulation examples are given to illustrate the effectiveness and advantages of the proposed results.

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)0.002
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: Methods · Consensus signal: none
Teacher disagreement score0.786
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.289
Teacher spread0.266 · 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