Fault Estimation and Fault-Tolerant Tracking Control for Multi-Agent Systems With Lipschitz Nonlinearities Using Double Periodic Event-Triggered Mechanism
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Bibliographic record
Abstract
This paper is concerned with the problem of fault estimation and fault-tolerant consensus tracking control for Lipschitz nonlinear multi-agent systems subject to external disturbance. In order to improve the communication efficiency of main network channels, two periodic event-triggered mechanisms are developed between sensor to observer and observer to controller, respectively. An event-triggered fault observer is developed to estimate existing faults and the estimated result is used for the design of a fault-tolerant controller to ensure system security. According to Lyapunov-Krasovskii theorem and the free-weighting matrix technique, the model gains of the observer and the controller can be obtained by solving a series of bilinear matrix inequalities (BMIs). To address the difficulty associated with BMIs, two iterative algorithms based on linear matrix inequalities (LMIs) are developed. Finally, a simulation example of satellite vehicles is given to illustrate the effectiveness of the obtained theoretical results.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.004 |
| 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 it