Edge-Based Communication-Triggered Formation Tracking Control With Application to Multiple Mobile Robots
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Bibliographic record
Abstract
This article considers the distributed leader–follower formation tracking control problem for a networked linear multiagent system (MAS) consisting of one nonautonomous leader and multiple homogeneous followers in a sample-data-based edge-event-triggering communication setting. A novel edge-state-estimate-based triggering function along with a triggering rule is proposed for each communication edge to regulate and reduce unnecessary data transmission. A new distributed formation tracking protocol is then developed for each follower based on only event-generator-regulated information. The formation tracking problem is reformulated as a stability analysis problem of a delayed system by defining the formation error dynamics. Lyapunov-based methods and linear matrix inequality (LMI) techniques are utilized to derive sufficient conditions for codesigning the event-generator and controller gains that ensure the asymptotic and exponential convergence of the closed-loop formation error dynamics. Numerical simulations and experimental implementations were carried out using a group of four unicycle-type mobile robots to demonstrate and validate the effectiveness 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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