Distributed Model Predictive Control with Connectivity-based Contracts
Abstract
Teams of mobile robots rely on continuous communication with their neighbors for coordination, yet most distributed model predictive control (DMPC) schemes assume the communication network stays connected rather than actively enforcing it. Adding such a guarantee is hard since the usual mathematical condition for connectivity is nonconvex and links every agent to every other, which is incompatible with a scalable distributed real-time controller. We propose a DMPC framework in which each agent is assigned a connectivity contract: a local region prescribing where its predicted positions may lie over the prediction horizon. The contracts are designed so that, as long as every agent stays within its own contract, the team is guaranteed to remain connected. Given the maintained contract graph, an agent builds its contract from a single exchange with its immediate neighbors, after which every agent solves its own optimization problem independently. We prove that the resulting closed-loop system maintains connectivity, avoids collisions, and respects local state and input constraints. Simulation and hardware experiments on miniature autonomous car-like robots demonstrate the approach.
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BibTeX
@misc{geurts2026distributed,
title = {{Distributed Model Predictive Control with Connectivity-based Contracts}},
author = {Jorit Geurts and Danilo Saccani and Melanie N. Zeilinger and Andrea Carron},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.19912},
eprint = {2609.19912},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.19912},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Distributed Model Predictive Control with Connectivity-based Contracts
AU - Jorit Geurts
AU - Danilo Saccani
AU - Melanie N. Zeilinger
AU - Andrea Carron
PY - 2026
DA - 2026/09/17
PB - arXiv
DO - 10.48550/arxiv.2609.19912
UR - https://arxiv.org/abs/2609.19912
AB - Teams of mobile robots rely on continuous communication with their neighbors for coordination, yet most distributed model predictive control (DMPC) schemes assume the communication network stays connected rather than actively enforcing it. Adding such a guarantee is hard since the usual mathematical condition for connectivity is nonconvex and links every agent to every other, which is incompatible with a scalable distributed real-time controller. We propose a DMPC framework in which each agent is assigned a connectivity contract: a local region prescribing where its predicted positions may lie over the prediction horizon. The contracts are designed so that, as long as every agent stays within its own contract, the team is guaranteed to remain connected. Given the maintained contract graph, an agent builds its contract from a single exchange with its immediate neighbors, after which every agent solves its own optimization problem independently. We prove that the resulting closed-loop system maintains connectivity, avoids collisions, and respects local state and input constraints. Simulation and hardware experiments on miniature autonomous car-like robots demonstrate the approach.
ER -