Formation Control and Target Tracking for a Class of Nonlinear Multi-Agent Systems Using Neural Networks
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Bibliographic record
Abstract
This paper proposes a neural network-based backstepping controller to address the distance-based formation control problem and target tracking for a class of nonlinear multiagent systems in Brunovsky form using rigid graph theory. The radial basis function neural network (RBFNN) is used to ensure the system stability in the presence of unknown nonlinearity and disturbance in the system dynamics. A Lyapunov function is used to derive the neural network (NN) weights tuning law. The uniform ultimate boundedness (UUB) of the formation distance errors is rigorously proven based on the Lyapunov stability theory. Finally, the effectiveness of the proposed method is shown using the simulation results on a class of nonlinear multi-agent systems. A comparison between the proposed distance-based method and the existing displacement-based method is conducted to evaluate the performance 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 | 0.001 |
| Open science | 0.000 | 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