Distributed Event-Triggered Quantized Fault-Tolerant Control of Linear Multiagent Systems With External Disturbances and Parameter Uncertainties
Why this work is in the frame
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Bibliographic record
Abstract
In this article, the issue of fault-tolerant leader-following consensus under a distributed dynamic event-triggered mechanism is addressed for linear multiagent systems (MASs) in the presence of unknown parameter uncertainties, external disturbances, and actuator faults, including loss of effectiveness and bias, in which the mechanism is with quantized state measurements. Due to the fact that information is transmitted via a bandwidth-limited communication network, a quantized control scheme with a uniform quantizer is introduced for leader-following consensus. In order to decrease the communication load and save the limited communication network resources, a distributed event-triggered mechanism is studied for leader-following consensus problem of linear MASs with quantized state measurements. In the presence of actuator faults, external disturbances, and unknown parameter uncertainties, an adaptive coupling gain for the controller is presented. Based on the Lyapunov function approach, the stability of the closed-loop system and the convergence of consensus errors are proved. Furthermore, the Zeno behavior is excluded for the triggering time sequences. Finally, simulation studies are given to verify the effectiveness of the proposed event-triggered fault-tolerant control scheme.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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