Event-Triggered Consensus Control for Multiagent Systems With Time-Varying Communication and Event-Detecting Delays
Why this work is in the frame
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Bibliographic record
Abstract
In this paper, using event-triggered control method, we investigate the consensus problem of a distributed single-integrator network with time-varying communication and event-detecting delays. We propose a consensus protocol as well as event-triggering conditions only based on local information. The event-triggering condition for each agent is detected periodically to determine whether its state used in the local feedbacks of the network will be updated or not. A sufficient condition for reaching state consensus is given and the convergence analysis is conducted by Lyapunov methods. The presented result reveals the effect of the change rate of time delays on consensus. Moreover, an experimental example of a multiagent networked system consisting of two-wheeled mobile robots is conducted to demonstrate the feasibility and effectiveness of our proposed consensus protocol.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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