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Record W2166774307 · doi:10.1002/acs.767

A pragmatic approach towards assessment of control loop performance

2003· article· en· W2166774307 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 Adaptive Control and Signal Processing · 2003
Typearticle
Languageen
FieldEngineering
TopicAdvanced Control Systems Optimization
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsBenchmark (surveying)PID controllerControl theory (sociology)Loop (graph theory)Controller (irrigation)Open-loop controllerComputer scienceProcess (computing)Control (management)Control engineeringSimple (philosophy)Step responseClosed loopEngineeringArtificial intelligenceMathematicsTemperature control

Abstract

fetched live from OpenAlex

Abstract In this paper, a pragmatic approach to process modelling and control loop performance assessment is proposed. We consider the following practical scenarios: the control loop of concern is operated by a simple controller such as PI/PID controller; a simple open‐loop/closed‐loop step response data is available or an open‐loop/closed‐loop step test can be readily performed; the control loop may be subject to significant disturbances and/or measurement noises. Our objectives are: (1) to estimate a continuous‐time process model from the step response data; (2) to assess control loop performance with a pragmatic benchmark in terms of both output performance and input variation, and identify practically attainable control loop performance. We will summarize the theory and algorithms developed for such a relatively comprehensive analysis. A number of simulation studies are presented to demonstrate the feasibility of the proposed methodology. Copyright © 2003 John Wiley & Sons, Ltd.

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 score0.963
Threshold uncertainty score0.556

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 communication0.0000.001
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 head0.008
GPT teacher head0.238
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