Neural adaptive control for leader–follower flocking of networked nonholonomic agents with unknown nonlinear dynamics
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Bibliographic record
Abstract
SUMMARY This paper is concerned with the leader–follower flocking problem of networked nonholonomic multi‐agent systems with non‐identical unknown nonlinear dynamics. The leader motion to be synchronized is also nonlinear and unknown. By employing the graph theory and a pinning control technique, a distributed neural adaptive control design is developed for the agents to achieve motion synchronization with the leader. The design is for a directed communication graph with a fixed topology. A collective potential function is used to maintain cohesion between the agents. On the basis of Lyapunov analysis, the developed neural flocking algorithm guarantees that all the agents’ headings and speeds are synchronized with the leader and collisions between the agents can be avoided. An illustrative example is given to show the effectiveness of the proposed control strategy. Copyright © 2013 John Wiley & Sons, Ltd.
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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 | 0.000 |
| 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