  This paper studies the problem of distributionally robust model predictive
control (MPC) using total variation distance ambiguity sets. For a
discrete-time linear system with additive disturbances, we provide a
conditional value-at-risk reformulation of the MPC optimization problem that is
distributionally robust in the expected cost and chance constraints. The
distributionally robust chance constraint is over-approximated as a simpler,
tightened chance constraint that reduces the computational burden. Numerical
experiments support our results on probabilistic guarantees and computational
efficiency.
