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Record W2106424973 · doi:10.1080/0020717042000267723

Reducing conservatism in the design of a robust gain-scheduled controller for non-linear chemical processes

2004· article· en· W2106424973 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

VenueInternational Journal of Control · 2004
Typearticle
Languageen
FieldEngineering
TopicStability and Control of Uncertain Systems
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsControl theory (sociology)ConservatismRobust controlController (irrigation)Lyapunov functionMathematicsRobustness (evolution)Computer scienceControl (management)Mathematical optimizationControl engineeringEngineeringControl systemNonlinear system

Abstract

fetched live from OpenAlex

A robust control approach has been used to design gain-scheduled controllers, which guarantee closed-loop stability and performance. The inherent conservatism of robust control results in smaller ranges of parameters that satisfy the design criteria and consequently in degraded performance. The main focus of this paper is to reduce the conservatism to enhance the efficiency of the robust design method. Two approaches, the use of parameter-dependent Lyapunov condition and calculation of saturation factor bounds, are proposed to reduce the conservatism of the controller design.

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: Empirical · Consensus signal: none
Teacher disagreement score0.864
Threshold uncertainty score0.378

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.025
GPT teacher head0.255
Teacher spread0.230 · 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