An unknown input observer-based decentralized fault detection and isolation for a class of large-scale interconnected nonlinear systems
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Bibliographic record
Abstract
In this paper, using a bank of decentralized nonlinear unknown input observers, a novel scheme for actuator fault detection and isolation of a class of large-scale interconnected nonlinear systems is presented. For each of the interconnected subsystems, a local nonlinear unknown input observer is designed without the need to communicate with other agents. Sufficient conditions for the observer existence are derived based on the Lyapunov stability theory. To facilitate the observer design, the achieved conditions are formulated in terms of a set of linear matrix inequalities and optimal gain matrices are obtained. By using both system output and its difference with the estimated output in observer equation, each local observer shows a high convergence rate. Simulation of an automated highway system is used to demonstrate the effectiveness of the proposed methodology.
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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 it