Dynamic Event-Triggered DMPC With Variable Prediction Horizon for Disturbed Nonlinear Multiagent Systems
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Bibliographic record
Abstract
This article investigates the formation stabilization problem of continuous-time nonlinear multiagent systems subject to state constraints, input constraints, and external disturbances. To solve this issue, a dynamic event-triggered distributed model-predictive control algorithm is developed, integrating a control configuration that simultaneously considers both the triggering scheme and the variable prediction horizon. Specifically, a dynamic event-triggered mechanism based on feasibility analysis is proposed to adaptively adjust the triggering threshold, thereby reducing computational and communication burdens while preventing Zeno behavior. Meanwhile, a variable prediction horizon scheme is designed for each agent to effectively shorten the prediction horizon of the involved optimal control problem, which reduces the computational complexity of the proposed algorithm. Furthermore, theoretical conditions are established to ensure the recursive feasibility and closed-loop stability of the algorithm. Finally, theoretical results are verified through a numerical example with comparison analysis.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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