  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.
