Interval state and sensor fault estimation based on unknown input observer and interval hull computation
Bibliographic record
Abstract
Abstract This paper investigates the problem of interval estimation of state and sensor fault simultaneously based on the unknown input decoupling technique and interval hull computation approach. To facilitate sensor fault estimation conveniently, it is first regarded as an auxiliary state, and the faulty system is transformed into an augmented descriptor system. Next, an unknown input observer is designed based on the P‐radius method for this augmented descriptor system to decouple some of the unknown system disturbance. Then, the bounds of the augmented state set effected by the undecoupled system uncertainties are calculated via an interval hull approximation technique. Finally, the effectiveness and practicality of this work are illustrated by a numerical example and a quadruple‐tank process system. This work demonstrates that by decoupling some unknown disturbances from the estimation error system, the zonotope's size, corresponding to state and sensor fault estimation, can be further reduced.
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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".