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Distributionally Robust Model Predictive Control With Total Variation Distance


Abstract:

This letter studies the problem of distributionally robust model predictive control (MPC) using total variation distance ambiguity sets. For a discrete-time linear system...Show More

Abstract:

This letter 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.
Published in: IEEE Control Systems Letters ( Volume: 6)
Page(s): 3325 - 3330
Date of Publication: 21 June 2022
Electronic ISSN: 2475-1456

Funding Agency:


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