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Record W2099321323 · doi:10.23919/acc.2004.1384429

Decentralized two-time-scale motions control using generalized sampled-data hold functions

2004· article· en· W2099321323 on OpenAlex
Amir G. Aghdam, Valery D. Yurkevich

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

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicModel Reduction and Neural Networks
Canadian institutionsConcordia University
Fundersnot available
KeywordsControl theory (sociology)DiscretizationLTI system theoryController (irrigation)Sampled data systemsDiscrete time and continuous timeComputer scienceScale (ratio)Decentralised systemLinear systemInvariant (physics)MathematicsControl systemControl (management)EngineeringMathematical analysisArtificial intelligence

Abstract

fetched live from OpenAlex

This paper presents a decentralized variation of the two-time-scale motions controller for linear time invariant systems. A method is proposed to change the structure of the system using discretization and generalized sampled-data hold functions, so that distinct local discrete-time controllers can be applied to each input-output agent. The resultant output feedback decentralized periodic controller has, in fact, a linear time-varying structure. Conditions under which the desired structure modification can be accomplished are given and the simulation results are also included.

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 categoriesInsufficient payload (model declined to judge)
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 score0.800
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.000

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 head0.049
GPT teacher head0.294
Teacher spread0.245 · 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

Quick stats

Citations1
Published2004
Admission routes1
Has abstractyes

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