On Zeno Behavior in Event-Triggered Finite-Time Consensus of Multiagent Systems
Bibliographic record
Abstract
This article studies Zeno behavior, where an infinite number of events occur in a finite-time interval, in the event-triggered multiagent systems that aim at achieving consensus in finite time. Two popular scenarios in the first-order multiagent systems with model-based event-triggered controllers are considered. One is that the event trigger in each agent samples the absolute information, and decides when to broadcast the information to its neighbors, and the other is that the update of control signals is only scheduled by the local event triggers via directly using the (combined) relative information. The events are triggered when the measurement error between the agent, and model states violates a given threshold function. Both cases are studied, where the threshold is generated by the agent state or by the model state. Then, sufficient conditions on the existence of Zeno behavior in event-triggered finite-time consensus of multiagent systems are provided. For the triggering conditions with the thresholds given by agent states, the system must exhibit Zeno behavior; while in the case of using model states, the existence of Zeno behavior is influenced by the properties of the communication topology, and the threshold functions. Finally, several simulations, and examples are provided to illustrate the effectiveness of the theoretical results.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".