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arXiv · 2507.14409

Collaborative Indirect Influencing and Control on Graphs using Graph Neural Networks

Max L. Gardenswartz·Brandon C. Fallin·Cristian F. Nino·Warren E. Dixon
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Abstract

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.

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Max L. Gardenswartz, Brandon C. Fallin, Cristian F. Nino, Warren E. Dixon. 2025-07-18. Collaborative Indirect Influencing and Control on Graphs using Graph Neural Networks. https://arxiv.org/abs/2507.14409

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