This paper presents experiments for embedded cooperative distributed model predictive control applied to a team of hovercraft floating on an air hockey table. The hovercraft collectively solve a centralized optimal control problem in each sampling step via a stabilizing decentralized real-time iteration scheme using the alternating direction method of multipliers. The efficient implementation does not require a central coordinator, executes onboard the hovercraft, and facilitates sampling intervals in the millisecond range. The formation control experiments showcase the flexibility of the approach on scenarios with point-to-point transitions, trajectory tracking, collision avoidance, and moving obstacles.
@article{arxiv.2409.13334,
title = {Cooperative distributed model predictive control for embedded systems: Experiments with hovercraft formations},
author = {Gösta Stomberg and Roland Schwan and Andrea Grillo and Colin N. Jones and Timm Faulwasser},
journal= {arXiv preprint arXiv:2409.13334},
year = {2025}
}