Adaptive, Fuzzy Boundary Observer for a Class of Euler-Bernoulli Beam Systems With Uncertainties
Bibliographic record
Abstract
In this article, 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. In particular, we consider the presence of unknown parameters and unstructured uncertainties that may appear in-domain and at the boundary, which makes the accurate estimation of the system state difficult. The objective of this article is to simultaneously estimate the system state and uncertainties. For unknown parameters, we decouple the estimation of the parameters from the estimation of the system state by means of the appropriate finite-dimensional backstepping-like transformation. On the other hand, we introduce an interval type-2 fuzzy logic system to approximate the unstructured uncertainties that have entered the system domain. Based on the finite-dimensional backstepping-like transformation and an interval type-2 fuzzy logic system, an adaptive boundary observer is designed to simultaneously estimate the system state and the system uncertainties. The results are proved by the Lyapunov stability theory.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".