Event-Triggered Consensus of Nonlinear Agents with Quantized Broadcasts: A Hybrid System Approach
Bibliographic record
Abstract
Information exchange among agents operating over a network, in practice, is restrained by limited communication bandwidth; this concern is often addressed by employing quantized broadcasts. In this paper, we study the problem of consensus of nonlinear multi-agent systems (MASs) over a directed network where the agents employ: a) encoders, that quantize relevant information prior to broadcasting, and b) decoders, that process this information upon arrival. The decision on the broadcast instant itself is made with the help of a dynamic event-triggering mechanism (ETM) in that the agents evaluate their respective event-triggering conditions intermittently at pre-designed sampling instants (which may be both aperiodic and asynchronous). Subsequently, the agents utilize model-based propagates of the decoded neighbor states in their control protocols to achieve consensus. The overall MAS is modeled using hybrid systems framework and the results are demonstrated through an illustrative example.
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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 | 0.001 |
| 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".