Multi-Layered Formation Control of Autonomous Marine Vehicles with Nonlinear Dynamics
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Bibliographic record
Abstract
This paper deals with a multi-layered formation control of autonomous marine vehicles (AMVs). Each AMV is considered as an agent and modeled with a nonlinear dynamics. The nonlinear dynamics include a square law drag in the velocity of the vehicle and saturation in the acceleration input. The system is stabilized using a rigid graph-based control approach. A Lyapunov candidate is chosen and proved that it satisfies conditions for an energy function of the formulated problem. Using the proposed Lyapunov energy function and related control law, an asymptotic stability with improved rate convergence of the system is rigorously proven. The required constraints for prevention of ambiguous formations that cause failure of convergence in the system have been developed. The simulation results illustrate the effectiveness of the proposed control law.
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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 | 0.001 |
| 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