Paper ID: 2203.12062

Distributionally Robust Model Predictive Control with Total Variation Distance

Anushri Dixit, Mohamadreza Ahmadi, Joel W. Burdick

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.

Submitted: Mar 22, 2022