  Distributed model predictive control methods for uncertain systems often
suffer from considerable conservatism and can tolerate only small uncertainties
due to the use of robust formulations that are amenable to distributed design
and optimization methods. In this work, we propose a distributed stochastic
model predictive control (DSMPC) scheme for dynamically coupled linear
discrete-time systems subject to unbounded additive disturbances that are
potentially correlated in time. An indirect feedback formulation ensures
recursive feasibility of the DSMPC problem, and a data-driven, distributed and
optimization-free constraint tightening approach allows for exact satisfaction
of chance constraints during closed-loop control, addressing typical sources of
conservatism. The computational complexity of the proposed controller is
similar to nominal distributed MPC. The approach is demonstrated in simulation
for the temperature control of a large-scale data center subject to randomly
varying computational loads.
