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- W4367000272 abstract "We study model predictive control (MPC) problems for stochastic LTI systems, where the noise distribution is unknown, compactly supported, and only observable through a limited number of i.i.d. noise samples. Building upon recent results in the literature, which show that distributional uncertainty can be efficiently captured within a Wasserstein ambiguity set, and that such ambiguity sets propagate exactly through the system dynamics, we start by formulating a novel Wasserstein Tube MPC (WT-MPC) problem, with distributionally robust CVaR constraints. We then show that the WT-MPC problem: (1) is a direct generalization of the (deterministic) Robust Tube MPC (RT-MPC) to the stochastic setting; (2) through a scalar parameter, it interpolates between the data-driven formulation based on sample average approximation and the RT-MPC formulation, allowing us to optimally trade between safety and performance; (3) admits a tractable convex reformulation; and (4) is recursively feasible. We conclude the paper with a numerical comparison of WT-MPC and RT-MPC." @default.
- W4367000272 created "2023-04-27" @default.
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- W4367000272 date "2023-04-24" @default.
- W4367000272 modified "2023-10-18" @default.
- W4367000272 title "Wasserstein Tube MPC with Exact Uncertainty Propagation" @default.
- W4367000272 doi "https://doi.org/10.48550/arxiv.2304.12093" @default.
- W4367000272 hasPublicationYear "2023" @default.
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