Periodic Event-Triggered Consensus Using Relative-State Measurements: A Hybrid System Approach
Bibliographic record
Abstract
Often in practical applications, such as coordinated motion of autonomous vehicles, multi-agent systems (MASs) utilize information obtained from sensors to accomplish complex tasks, asynchronously. In this work, we consider the problem of consensus where the agents obtain relative-state measurements, at their own sampling frequencies, and employ a distributed event-triggered protocol to dictate when to update control. For the designed event-triggered protocol, only local intermittent relative-state measurements are utilized, where they are obtained and evaluated only at pre-determined event-monitoring instants; these instants are governed by sampling periods whose bounds are explicitly pre-computed, individually, for each agent. Hence, the designed protocol is inherently asynchronous and avoid Zeno behaviour by construction. To cope with the continuous-time dynamics of the agents and discrete-time sensing and controller updates, the overall MAS is modelled using the hybrid system framework. A numerical example is provided to clearly demonstrate the effectiveness of the designed protocol.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".