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Record W2171757527 · doi:10.1109/cdc.2008.4739407

A switching supervisory control design for uncertain discrete time-delay systems

2008· article· en· W2171757527 on OpenAlexaff
Kaveh Moezzi, Ahmadreza Momeni, Amir G. Aghdam

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicStability and Control of Uncertain Systems
Canadian institutionsConcordia University
Fundersnot available
KeywordsControl theory (sociology)Supervisory controlStability (learning theory)Discrete time and continuous timeComputer scienceBounded functionScheme (mathematics)Upper and lower boundsSet (abstract data type)Control systemAdaptive controlControl (management)MathematicsEngineering

Abstract

fetched live from OpenAlex

This paper presents an adaptive switching supervisory control scheme for highly uncertain discrete-time systems with time-varying state delay and time-varying parameters. The uncertainties appear in the system matrices and the system is assumed to be subject to the external bounded disturbances. It is supposed that a set of stabilizing controllers are available (which are designed off-line) to stabilize the system in the whole uncertain parameter space. To find a supervisory control scheme, it is initially assumed that the system parameters and delay are fixed. A switching algorithm is then proposed to stabilize the system. Next, by modifying the proposed algorithm, the stability analysis of the system with time-varying parameters and time-varying delay is carried out. Furthermore, an upper bound on the permissible rate of change of the system parameters and delay to maintain stability of the closed-loop system is obtained. Simulation results are presented to show the efficacy of the proposed switching scheme.

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 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 score0.975
Threshold uncertainty score0.962

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.036
GPT teacher head0.215
Teacher spread0.179 · 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 teacher head, 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

Citations1
Published2008
Admission routes1
Has abstractyes

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