Self-Triggered Min–Max DMPC for Asynchronous Multiagent Systems With Communication Delays
Why this work is in the frame
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Bibliographic record
Abstract
This article studies the formation stabilization problem of asynchronous nonlinear multiagent systems (MAS) subject to parametric uncertainties, external disturbances, and bounded time-varying communication delays. A self-triggered min–max distributed model predictive control (DMPC) approach is proposed to address this problem. At triggering instants, each agent solves a local min–max optimization problem based on local system states and predicted states of neighbors, determines its next triggering instant, and broadcasts its predicted state trajectory to the neighbors. As a result, the communication load is greatly alleviated while retaining robustness and comparable control performance compared to periodic DMPC algorithms. In order to handle time-varying delays, a novel consistency constraint is incorporated into each local optimization problem to restrict the deviation between the newest predicted states and previously broadcasted predicted states. Consequently, each agent can utilize previously predicted states of its neighbors to achieve cooperation in the presence of the asynchronous communication and time-varying delays. The proposed algorithm’s recursive feasibility and MAS’s closed-loop stability at triggering instants are proven. Finally, numerical simulations are conducted to verify 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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