Actuator Fault Isolation and Estimation for Uncertain Nonlinear Systems
Bibliographic record
Abstract
This paper considers observer based actuator fault isolation schemes for a class of uncertain nonlinear systems. To deal with a broader class of uncertain non-linearities, we propose novel diagnostic observers, which combine Thau's observer with sliding mode observers and are primarily designed for actuator fault diagnostic purposes. The uncertain nonlinearities that can be attacked may include both Lipschitz uncertain nonlinearities and those uncertain nonlinearities that are not Lipschitz but satisfy certain matching conditions. The design of observer boils down to the solving of LMIs, which can easily be done using the Matlab LMI toolbox. Using the proposed observers, two actuator fault isolation schemes are designed. Unlike the existing techniques, using only m observers in the first approach and only one observer in the second approach, our proposed schemes can isolate any number of actuator faults occurring at the same time. In addition, both proposed schemes are capable of estimating the faults.
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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".