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Record W2029001775 · doi:10.1002/acs.1158

Iterative adaptive robust control of multivariable CD processes

2009· article· en· W2029001775 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueInternational Journal of Adaptive Control and Signal Processing · 2009
Typearticle
Languageen
FieldEngineering
TopicIterative Learning Control Systems
Canadian institutionsUniversity of British Columbia
FundersUniversity of British Columbia
KeywordsMultivariable calculusControl theory (sociology)DiscretizationTransfer functionAdaptive controlRobust controlComputer scienceBandwidth (computing)Controller (irrigation)Iterative learning controlSpatial frequencyControl systemControl engineeringMathematicsEngineeringControl (management)Artificial intelligence

Abstract

fetched live from OpenAlex

Abstract In this paper we present a novel adaptive robust control approach to the multivariable cross‐directional (CD) process of continuous web manufacturing. The common assumption of spatial frequency decomposition (SFD) is used to allow the process analysis in terms of a family of single‐input single‐output (SISO) transfer functions across the spatial frequencies. We then apply discretized Windsurfing adaptive robust control to each individual separated spatial frequency, starting with a stable initial model and a robust stabilizing controller at each spatial frequency. This approach allows the 2D bandwidth of the closed‐loop system to be increased progressively at each spatial frequency through an iterative relevant system identification and control design procedure. The method deals with both model uncertainty and measurement noise issues. Simulation results are given to illustrate the performance of the applied method. Copyright © 2009 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)0.000
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 score0.976
Threshold uncertainty score0.958

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.012
GPT teacher head0.227
Teacher spread0.215 · 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