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Record W2292009972 · doi:10.1109/cdc.2015.7403307

Robust regional eigenvalue assignment by dynamic state-feedback control for nonlinear continuous-time systems

2015· article· en· W2292009972 on OpenAlexaff
William A. Baker, Susan C. Schneider, Edwin E. Yaz

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicStability and Control of Uncertain Systems
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsControl theory (sociology)Eigenvalues and eigenvectorsLipschitz continuityNonlinear systemLinear matrix inequalityFull state feedbackRobust controlObserver (physics)Norm (philosophy)Controller (irrigation)Computer scienceLinear systemStability (learning theory)MathematicsMathematical optimizationControl (management)

Abstract

fetched live from OpenAlex

This paper uses the concept of D-stability to place the eigenvalues of the linear component of both the controller and observer within separate circular regions in order to design a robust dynamic feedback controller using linear matrix inequality (LMI) techniques to accommodate Lipschitz type nonlinearities for continuous-time systems. Given a feasible result for both the controller and observer design, the regions will be separated in such a way that the state estimation error goes to zero much faster than the state while guaranteeing stability. This design technique is extended to incorporate the H2-norm property. The method is applied to two nonlinear systems to illustrate the design procedure.

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 categoriesMeta-epidemiology (narrow)
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 score0.957
Threshold uncertainty score1.000

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 head0.019
GPT teacher head0.210
Teacher spread0.191 · 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.

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
Published2015
Admission routes1
Has abstractyes

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