Adaptive Fuzzy Descriptor Sliding Mode Observer‐based Sensor Fault Estimation for Uncertain Nonlinear Systems
Bibliographic record
Abstract
Abstract A sensor fault estimation method is proposed for a general class of uncertain nonlinear systems. Taking the sensor faults as auxiliary states, an augmented descriptor system is constructed. For this augmented descriptor system, an adaptive fuzzy proportional‐derivative sliding mode observer is designed to estimate the sensor faults and states, simultaneously. Adaptive fuzzy systems based on the universal approximation theorem are used to approximate the nonlinearities. Thus, the nonlinearities can be unknown and the Lipschitz assumption can be utterly relieved. To relax the requirement of knowing the bound of uncertainties, an adaptive gain for the sliding mode term is used. The semi‐global asymptotic convergence of the proposed fault estimation observer is proved using the Lyapunov approach. The proposed method is applied to a highly nonlinear mathematical model with both bounded and unbounded sensor faults. Simulation results show the satisfactory performance of the proposed method.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".