Model-based event/self-triggered fixed-time consensus of nonlinear multi-agent systems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
To address model-based event-triggered fixed-time consensus of nonlinear multi-agent systems with both fixed topology and switching topologies, a novel model-based event-triggered protocol is presented. In the proposed protocol, agents in multi-agent systems update controllers by utilizing estimated state values in the triggered intervals. And for reducing the burdens of bandwidth further, the model-based event-triggered protocol is extended to self-triggered protocol which does not need continuous communication in triggered intervals. It is proven that Zeno behavior is excluded. Our main contributions are giving a novel distributed model-based event-triggered protocol for fixed-time consensus and extending it into a self-triggered protocol. Finally, a couple of simulation examples are provided to verify the effectiveness of the proposed protocols.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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