Actuator Fault Detection and Estimation for a Class of Hyperbolic PDEs Using Filter-Based Observer
Bibliographic record
Abstract
This paper is concerned with simultaneous actuator fault detection and estimation problems for a class of hyperbolic partial integral-differential equations (PIDEs) with boundary controlled and boundary observation. Based on a Luenberger-type observer, the paper studies fault detectability and gives detectability conditions. In presence of actuator fault, the traditional boundary observer based backstepping techniques can only detect a fault occurrence rather than estimate the fault parameter. In this case, this paper developed two novel plant state observers equipped with corresponding fault parameter estimator (parameter tuning update law). The first observer is designed without consideration of model uncertainties or external disturbances based on the observation error system and the fault parameter tuning update law is developed in cooperation with a simple error target system obtained through backstepping transformation. Moreover, with consideration of bounded model uncertainties and disturbances, this paper also developed a filter-based observer. It can also be seen that the second method is easily extended for sensor fault estimation. For both proposed methods, only the boundary measurement and input signals are needed. Finally, a representative example is given to verify theoretical results of this paper.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".