Formation Control with Dynamic Non-Autonomous Leader Using Sampled-Data Event-Triggered Communication
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Bibliographic record
Abstract
This paper addresses the distributed formation tracking control for general linear multi-agent systems with a dynamic non-autonomous leader in a sampled-data setting. First, a novel locally-computable state-estimate-based event-generator is proposed for each follower agent to regulate and reduce unnecessary data transmissions. Second, a distributed formation protocol is proposed that only uses the triggered sampled information such that the formation tracking problem can be formulated as a stability analysis problem of the closed-loop formation error dynamics. Sufficient conditions in the form of linear matrix inequalities (LMIs) that guarantee the coexistence of a valid formation controller and an event-triggered communication mechanism are then derived using Lyapunov-based stability analysis methods. Finally, numerical simulations for multiple mobile robots formations were conducted to demonstrate the effectiveness and advantages of 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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.002 | 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