Distributionally Robust Model Predictive Control with Total Variation Distance

Anushri Dixit; Mohamadreza Ahmadi; Joel W. Burdick

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arXiv · 2203.12062

Distributionally Robust Model Predictive Control with Total Variation Distance

Anushri Dixit·Mohamadreza Ahmadi·Joel W. Burdick

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Abstract

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.

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BibTeX RIS

Anushri Dixit, Mohamadreza Ahmadi, Joel W. Burdick. 2022-03-22. Distributionally Robust Model Predictive Control with Total Variation Distance. https://arxiv.org/abs/2203.12062

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