In this paper, we present a data-driven distributed model predictive control (MPC) scheme to stabilise the origin of dynamically coupled discrete-time linear systems subject to decoupled input constraints. The local optimisation problems solved by the subsystems rely on a distributed adaptation of the Fundamental Lemma by Willems et al., allowing to parametrise system trajectories using only measured input-output data without explicit model knowledge. For the local predictions, the subsystems rely on communicated assumed trajectories of neighbours. Each subsystem guarantees a small deviation from these trajectories via a consistency constraint. We provide a theoretical analysis of the resulting non-iterative distributed MPC scheme, including proofs of recursive feasibility and (practical) stability. Finally, the approach is successfully applied to a numerical example.
@article{arxiv.2202.12764,
title = {Data-driven distributed MPC of dynamically coupled linear systems},
author = {Matthias Köhler and Julian Berberich and Matthias A. Müller and Frank Allgöwer},
journal= {arXiv preprint arXiv:2202.12764},
year = {2023}
}