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Adaptive Boundary Observer Design for Euler-Bernoulli Beam Systems with Parameter Uncertainties

2024· article· en· W4402260532 on OpenAlexaff
Xunyuan Yin, X. Xiaodong, Stevan Dubljević

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicStability and Controllability of Differential Equations
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsBernoulli's principleObserver (physics)Euler's formulaBoundary (topology)Boundary value problemComputer scienceControl theory (sociology)MathematicsMathematical analysisPhysicsArtificial intelligence

Abstract

fetched live from OpenAlex

In this paper, the problem of state estimation for a class of Euler-Bernoulli beam systems is considered, where the state over the length of the beam is estimated using only measurements at the boundary points of the Euler-Bernoulli beam system. We particularly consider unknown parameters that may occur in the domain and at the boundary, which creates difficulties in accurately estimating the system state. The goal of this paper is to simultaneously estimate the system state and the parameter uncertainties. The crucial element in the design process of the adaptive observer for the Euler-Bernoulli beam system in this study is the introduction of the appropriate finite-dimensional backstepping-like transformation, based on which the design of the parameter adaptive law can be decoupled from the choice of the state estimator. By Lyapunov stability analysis, it can be concluded that the observer converges exponentially under persistent excitation conditions. Furthermore, the effectiveness of the observer is corroborated through numerical simulations.

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)0.000
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 score0.949
Threshold uncertainty score0.585

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0000.000
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 head0.042
GPT teacher head0.231
Teacher spread0.189 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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