Event-Triggered State Estimation of Linear Systems Using Moving Horizon Estimation
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Bibliographic record
Abstract
In this brief, a problem of event-triggered state estimation for networked linear systems is investigated. We consider that the stochastic system disturbances and noise are bounded and moving horizon estimation (MHE) is used to handle these constraints. We establish an event-based state estimation mechanism that aims to provide good state estimates while reducing the frequencies of both the evaluation of the state estimator and networked communication between the plant and the estimator. An event-triggering condition is used to govern the evaluation of the MHE-based estimator and the use of networked communication. An MHE-based estimator is designed to provide state estimates when there is an event. Stability analysis of the estimation error dynamics is carried out for the proposed event-triggered estimation mechanism. The effectiveness and the applicability of the proposed method are demonstrated through numerical simulations and an experiment.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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