Distributionally Robust Model Predictive Control With Total Variation Distance

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 distribu...

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Bibliographic Details
Published inIEEE control systems letters Vol. 6; pp. 3325 - 3330
Main Authors Dixit, Anushri, Ahmadi, Mohamadreza, Burdick, Joel W.
Format Journal Article
LanguageEnglish
Published IEEE 2022
Subjects
Costs
Optimization
predictive control for linear systems
Q measurement
Random variables
Reactive power
Robustness
Stochastic optimal control
uncertain systems
Uncertainty
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Summary: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.
ISSN:2475-1456
2475-1456
DOI:10.1109/LCSYS.2022.3184921