Adaptive NN-Based Event-Triggered Consensus for Linear Multiagent Systems With Uncertainties
Why this work is in the frame
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Bibliographic record
Abstract
This paper explores the leader-following consensus of linear multi-agent systems with matched uncertainties under undirected graph. Firstly, we employ a neural network (NN) to approximate the nonlinear uncertainties. Then, an adaptive NN-based event-triggered (ET) feedback control scheme is designed. This scheme mitigates the chattering effect resulting from high-frequency switching by incorporating an adaptive boundary layer method. Notably, the proposed dynamic triggering function relies only on agents' local state, without continuous communication with neighboring agents. It is theoretically shown that consensus error is ultimately uniformly bounded. Additionally, Zeno behavior is also shown to be excluded. Finally, two numerical examples are presented to confirm the theoretical results.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| 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