Data-driven distributed MPC of dynamically coupled linear systems

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 decoupled input constraints. The local optimisation problems solved by subsystems rely on adaptation Fundamental Lemma Willems et al., allowing parametrise system trajectories using only measured input-output data without explicit knowledge. For predictions, communicated assumed neighbours. Each subsystem guarantees small deviation from these via consistency constraint. We provide theoretical analysis resulting non-iterative MPC scheme, including proofs recursive feasibility and (practical) stability. Finally, approach is successfully applied numerical example.