Konstantinos Prattis

Stochastic Nonlinear Model Predictive Control with Gaussian Mixture Uncertainty Propagation

It is shown that the state distribution of the system can be tractably approximated over time by Gaussian mixture distributions, with formal error bounds in Wasserstein distance, which yields closed-form expressions for expected costs and chance constraints.