Robust fault detection filter for non-linear state-delay networked control system
Bibliographic record
Abstract
A robust fault detection filter (RFDF) problem is investigated for a non-linear state-delay networked control system (NCS) with model uncertainty and data packet dropout. In both sensor-to-controller link and controller-to-actuator link, the random data packet dropouts are described by two variables, which obey the Bernoulli distribution. An observer-based RFDF is presented as a residual generator, and the residual dynamical system is modelled as a novel stochastic non-linear NCS with state-delay, model uncertainty, external disturbance. A performance index is proposed to deal with the robustness issue, which is to enhance the robustness of the residual generator against network-induced uncertainties and disturbances, without significant loss of the sensitivity of faults. Sufficient condition for asymptotically mean-square stable of this residual dynamical system is derived. Then, the desired RFDF is obtained, which is constructed in terms of certain linear matrix inequality. Finally, two numerical examples are employed to illustrate that the proposed approach performs better than the existing approaches.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".