Data-driven distributionally robust MPC using the Wasserstein metric

Understand

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.