Event-Triggered Optimal Dynamic Formation of Heterogeneous Affine Nonlinear Multiagent Systems
Why this work is in the frame
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Bibliographic record
Abstract
This article studies an optimal dynamic formation problem for heterogeneous affine nonlinear systems. The nonidenticality in agents and the requirement for dynamic spatial reconfiguration make it a challenging task to coordinate different types of agents to maintain an optimized formation shape. In an architecture of event-triggered decision and control, this article investigates how to fulfill dynamic formation by distributively optimizing a team cost function. The basic idea is to design a decision unit for each agent to generate an implicit trajectory as a servo signal, based on which a control unit is designed with a displacement-gradient-based law to achieve the desired local solution. Typical heterogeneous characteristics including different nonlinearities and nonidentical dimensions are dealt with in a unified framework. It is shown that with the proposed triggering mechanisms, the optimal dynamic formation problem can be solved by a distributed control law with only intermittent communication. In theory, the properties of convergence of trajectory tracking errors, optimality of the team solution, and Zeno-freeness of event-triggered mechanisms are proved. Two simulation examples are given to verify the proposed method.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 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