Formation Control for a Class of Nonlinear Multi-Agent Systems Using Three-Layer Neural Networks
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Bibliographic record
Abstract
This paper considers a leader-following formation control problem for a class of second-order, uncertain, input-affine, nonlinear multi-agent systems modeled by a directed graph. A three-layer neural network (NN) is proposed with an input layer, two hidden layers, and an output layer to approximate an unknown nonlinearity. The NN weights tuning laws were derived using the Lyapunov theory. The leader-following and formation control problem was addressed using a robust integral of the sign of the error (RISE) feedback and a NN-based control. The RISE feedback term compensates for an unknown leader dynamics and the bounded disturbance in the agent error dynamics. The NN-based term compensates for the unknown nonlinearity in the dynamics of agents. Semi-global asymptotic tracking results were rigorously proven using the Lyapunov stability theory. The numerical simulation results show 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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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