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Record W2119091456 · doi:10.1109/ecc.2015.7330596

State feedback output regulation for a class of hyperbolic PDE systems

2015· article· en· W2119091456 on OpenAlexaff
Xiaodong Xu, Biao Huang, Stevan Dubljević

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicStability and Controllability of Differential Equations
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsControl theory (sociology)RegulatorMathematicsRiccati equationLinear-quadratic regulatorExponential stabilityLyapunov functionStability (learning theory)Work (physics)Linear systemApplied mathematicsOptimal controlNonlinear systemMathematical analysisDifferential equationControl (management)Computer scienceMathematical optimizationEngineering

Abstract

fetched live from OpenAlex

This work focuses on the development of a state feedback regulator for a class of first order hyperbolic PDE systems with spatially varying coefficients and single point observation. The plant is assumed to be exponentially stabilizable and driven by a linear finite dimensional exosystem which is neutral stable and generates the reference signal and disturbance for the plant. The regulator design problem is to control the fixed plant such that the plant output tracks the reference signal and/or rejects the disturbance. Under the standard assumption of stabilizability, this work shows that the solvability of a constrained Sylvester equation is sufficient to guarantee the solvability of the regulator problem. Moreover, the Riccati and Lyapunov equations are utilized to provide a choice of stabilizing feedback gain which guarantees the closed-loop stability. An adequate numerical example of constant tracking for the first order hyperbolic PDE system with spatially varying coefficients is explored within the proposed regulator design.

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)0.000
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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.037
GPT teacher head0.230
Teacher spread0.193 · 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

Citations4
Published2015
Admission routes1
Has abstractyes

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