This paper presents a novel approach to solving the indirect influence problem in networked systems, in which cooperative nodes must regulate a target node with uncertain dynamics to follow a desired trajectory. We leverage the message-passing structure of a graph neural network (GNN), allowing nodes to collectively learn the unknown target dynamics in real time. We develop a novel GNN-based backstepping control strategy with formal stability guarantees derived from a Lyapunov-based analysis. Numerical simulations are included to demonstrate the performance of the developed controller.
@article{arxiv.2507.14409,
title = {Collaborative Indirect Influencing and Control on Graphs using Graph Neural Networks},
author = {Max L. Gardenswartz and Brandon C. Fallin and Cristian F. Nino and Warren E. Dixon},
journal= {arXiv preprint arXiv:2507.14409},
year = {2025}
}
Comments
arXiv admin note: substantial text overlap with arXiv:2503.15360