Adaptive Distributed Lyapunov-Based Model Predictive Control for Multi-UAV Formation Tracking with Weighted Directed Graphs
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Bibliographic record
Abstract
This paper tackles the formation tracking control problem of multiple unmanned aerial vehicles (UAVs) with a weighted directed communication graph. A novel adaptive fully distributed model predictive control (DMPC) framework is proposed, aiming to not only ensure closed-loop stability and feasibility of the local optimization problem, but also moderate the computation and communication consumption. Each local controller comprises an outer translation control loop and an inner rotation control loop. The outer loop employs Lyapunov-based model predictive control (MPC) to determine optimal translation control actions subject to input constraints. The MPC problem is formulated using only the relative neighborhood formation error between itself and its neighbors, thereby substantially reducing the total communication traffic. An adaptive estimator is also incorporated to estimate the future evolution of neighboring vehicles. Through closed-loop analysis, sufficient stability conditions regarding the selection of control parameters are established. Simulation results are provided to demonstrate the effectiveness of the proposed design.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| 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