Robust state estimation for a class of hyperbolic systems with boundary sensor uncertain parameter
Bibliographic record
Abstract
In this work, we propose a novel framework for the state estimation of first-order hyperbolic partial integral differential equation (PIDE) systems in the presence of multiplicative sensor uncertain parameter and uncertain exogenous disturbance. We consider the sensor uncertainty to be a sensor fault. In this context, a classical Luenberger observer no longer applies since no prior information is available to compensate the influence of uncertain parameters. Moreover, the estimation problem becomes a nonlinear problem due to the coupling between the uncertainty and the plant state. We develop a new adaptive law for the estimation of sensor uncertain parameter and embed the proposed laws into the state observer design. By selecting an appropriate Lyapunov function, we prove that the state estimation and parameter estimation error exponentially converge to an arbitrarily small neighborhood of the origin despite unknown disturbance. The effectiveness of the proposed method is studied via numerical simulation.
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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".