Secure adaptive fuzzy tracking control for a class of nonlinear systems under actuator and sensor faults and denial‐of‐service attacks based on event‐triggered mechanism
Bibliographic record
Abstract
Abstract The main focus of this article is to investigate the secure adaptive fuzzy tracking control (SAFTC) scheme for a class of uncertain nonlinear systems, with special presence of actuator and sensor faults and denial‐of‐service (DoS) attacks. The proposed method integrates switching gain observers, fuzzy logic systems (FLS), and event‐triggered control and introduces a new algorithm and dynamic gain to optimize tracking performance and resilience against disturbances, multiple faults, and DoS attacks. In addition, it is demonstrated that the proposed control scheme ensures the closed‐loop system remains bounded and the error signal asymptotically converges to a neighborhood around the origin. Finally, results for the proposed method applied to a class of unknown nonlinear systems are presented to back theoretical results and their effectiveness.
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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 | 0.001 | 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".