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Record W4256641306 · doi:10.23952/jnva.2.2018.1.06

Euclidean space controllability conditions and minimum energy problem for time delay systems with a high gain control

2018· article· en· W4256641306 on OpenAlexvenueno aff
Valery Y. Glizer

Bibliographic record

VenueJournal of Nonlinear and Variational Analysis · 2018
Typearticle
Languageen
FieldEngineering
TopicStability and Control of Uncertain Systems
Canadian institutionsnot available
Fundersnot available
KeywordsControllabilitySpace (punctuation)Euclidean spaceControl theory (sociology)High-gain antennaControl (management)Computer scienceEnergy (signal processing)MathematicsTopology (electrical circuits)Applied mathematicsMathematical analysisPhysicsStatisticsCombinatoricsArtificial intelligence

Abstract

fetched live from OpenAlex

In this paper, a non-autonomous differential system with state delays and with a large coefficient for the control (high gain control) is considered. By a proper control transformation, this system is converted into equivalent time delay system singularly perturbed by a small positive parameter. The latter system, is decomposed asymptotically into two much simpler parameter-free subsystems, slow and fast ones. The slow subsystem has delays in the state and control variables, while the fast subsystem is delay-free. Based on the assumption of the Euclidean space controllability of the slow subsystem, the Euclidean space controllability of the transformed and original systems is established for all sufficiently small values of the parameter of singular perturbation. Also, the minimum energy control problem for the transformed system is considered. Asymptotic behaviour with respect to the small parameter of the solution to this problem is studied. An illustrative example is presented.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.005
GPT teacher head0.205
Teacher spread0.201 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations10
Published2018
Admission routes1
Has abstractyes

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