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Record W1948852290 · doi:10.1002/acs.2292

Asymptotic rejection of nonvanishing disturbances despite plant–model mismatch

2012· article· en· W1948852290 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 · 2012
Typearticle
Languageen
FieldEngineering
TopicIterative Learning Control Systems
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsParametrization (atmospheric modeling)Control theory (sociology)PolynomialMinificationMathematicsApplied mathematicsInternal modelAdaptive controlClosed loopControl (management)Computer scienceMathematical optimizationEngineeringControl engineeringMathematical analysisPhysicsArtificial intelligence

Abstract

fetched live from OpenAlex

SUMMARY A direct adaptive control methodology for the rejection of unmeasured nonvanishing disturbances is proposed. The approach uses the framework of polynomial RST controllers and relies on the internal model principle with additional degrees of freedom provided by the Q parametrization. The parameters of the Q polynomial are adapted using minimization of the closed‐loop output error. Asymptotic rejection of unmeasured nonvanishing disturbances is guaranteed under the assumption that the closed‐loop plant remains stable. A simulation example illustrates the theoretical developments. Copyright © 2012 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 score0.680
Threshold uncertainty score0.484

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 head0.011
GPT teacher head0.221
Teacher spread0.210 · 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