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Record W2099586509 · doi:10.1109/icma.2005.1626615

Stabilization of a class of remote control systems and its robust stability analysis

2006· article· en· W2099586509 on OpenAlexaff
Ya‐Jun Pan, Jason Gu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicStability and Control of Uncertain Systems
Canadian institutionsDalhousie University
Fundersnot available
KeywordsControl theory (sociology)Parametric statisticsCascadeRobust controlNonlinear systemStability (learning theory)Computer scienceRobustness (evolution)Bounded functionController (irrigation)Norm (philosophy)Control systemControl engineeringControl (management)MathematicsEngineering

Abstract

fetched live from OpenAlex

In this paper, the stabilization of one class of remote control systems with time delays is first analyzed and then designed by using LMI techniques. Its robust design with respect to system parametric uncertainties, and its robust analysis with respect to nonlinear additive uncertainties and time delay uncertainties are discussed. The system under investigation is a cascade system with two subsystems, controlled by a remote controller with static gains. The motivation of this work is to explore the problem of distributed networked control systems; here we start the discussion of a simple cascade system. Static controller designs based on delay-dependent stability conditions are presented, which is proven to be less conservative than the conventional one. This design is then extended to the case when parametric uncertainties exist. Furthermore, sufficient stability conditions are derived for the system with norm-bounded nonlinear additive uncertainties and time delay variations. Finally, simulation examples are presented to show the effectiveness of the proposed method and to demonstrate the stability condition test for uncertain systems.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
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 head0.013
GPT teacher head0.196
Teacher spread0.182 · 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 designTheoretical or conceptual
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

Citations1
Published2006
Admission routes1
Has abstractyes

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