Adaptive Boundary Observer Design for Euler-Bernoulli Beam Systems with Parameter Uncertainties
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".