  This paper presents a distributionally robust stochastic model predictive
control (SMPC) approach for linear discrete-time systems subject to unbounded
and correlated additive disturbances. We consider hard input constraints and
state chance constraints, which are approximated as distributionally robust
(DR) Conditional Value-at-Risk (CVaR) constraints over a Wasserstein ambiguity
set. The computational complexity is reduced by resorting to a tube-based MPC
scheme with indirect feedback, such that the error scenarios can be sampled
offline. Recursive feasibility is guaranteed by softening the CVaR constraint.
The approach is demonstrated on a four-room temperature control example.
