Observer-based strategies for actuator fault detection, isolation and estimation for certain class of uncertain nonlinear systems
Bibliographic record
Abstract
Two observer-based actuator fault isolation schemes for a class of uncertain nonlinear systems have been presented. To deal with a broader class of uncertain nonlinearities than previously considered, novel diagnostic observers are proposed, which combine two nonlinear observer design strategies, namely Thau's observer with sliding-mode observer concepts. The proposed observers are primarily designed for actuator fault diagnostic purposes. The nonlinearities that can be attacked may include both Lipschitz nonlinearities and those uncertain nonlinearities that are not Lipschitz but satisfy certain matching conditions. Design of the proposed observer boils down to solving a set of linear matrix inequalities (LMIs), which can easily be accomplished using the Matlab's LMI toolbox. Using the proposed observers, two actuator fault isolation schemes are designed. Unlike the existing techniques, using m observers (where m is the number of actuators) in the first approach, and only one observer in the second approach, the proposed schemes can isolate any number of actuator faults occurring at the same time. In addition, both the proposed schemes are capable of estimating the shape of the faults which is useful for fault accommodation purposes. A numerical example is provided to show the effectiveness of the proposed model-based actuator fault isolation strategies. The simulation results confirm that the two proposed techniques are effective in dealing with robust actuator fault detection, isolation and estimation in the studied class of nonlinear systems.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".