Papers › Data-driven distributionally robust MPC using the Wasserstein metric
Data-driven distributionally robust MPC using the Wasserstein metric
Zhengang Zhong, Ehecatl Antonio del Rio-Chanona, Panagiotis Petsagkourakis
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A data-driven MPC scheme is proposed to safely control constrained stochastic linear systems using distributionally robust optimization. Distributionally robust constraints based on the Wasserstein metric are imposed to bound the state constraint violations in the presence of process disturbance. A feedback control law is solved to guarantee that the predicted states comply with constraints. The stochastic constraints are satisfied with regard to the worst-case distribution within the Wasserstein ball centered at their discrete empirical probability distribution. The resulting distributionally robust MPC framework is computationally tractable and efficient, as well as recursively feasible. The innovation of this approach is that all the information about the uncertainty can be determined empirically from the data. The effectiveness of the proposed scheme is demonstrated through numerical case studies.
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