Design of a TS Based Fuzzy Nonlinear Unknown Input Observer with Fault Diagnosis Applications
Bibliographic record
Abstract
An approach for nonlinear unknown input observer (NUIO) design for nonlinear systems representable by a Takagi-Sugeno (TS) fuzzy system. The proposed NUIO design for TS fuzzy systems is carried out for two cases: 1) the premise variables do not depend on the unmeasured state variables; and 2) the premise variables depend on the unmeasured state variables. Sufficient conditions for the existence of NUIOs are derived, and an LMI based observer design strategy is proposed. Once the NUIOs are designed, fault detection and isolation problems for nonlinear systems described by TS fuzzy systems using the new NUIO is presented. To solve the actuator fault isolation problem, a system structure with two groups of inputs where one group is treated as unknown inputs is developed. A bank of NUIOs is then designed in order to investigate the following fault diagnosis problems: 1) How can the NUIOs be used for detecting faults? 2) Under what conditions is it possible to isolate single and/or multiple faults? 3) What is the maximum number of faults that can be isolated simultaneously? 4) How can multiple fault isolation be achieved? The article presents an NUIO based fault detection scheme for problem 1, gives sufficient conditions for problem 2, determines the maximum number of faults that can be isolated for problem 3, and proposes a fault diagnosis scheme using a bank of NUIOs to solve problem 4.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".