**Dr. Hossein Sartipizadeh**

*Postdoctoral Fellow*, University of Texas at Austin

*Visiting Scientist*, Aeronautics & Astronautics, University of Washington Seattle

Tuesday, June 12, 2018, 2–3 PM

ECE, Room 118

**Talk abstract:** In this talk we focus on the sampling-based techniques, including the well-known scenario approach for stochastic/robust control of uncertain dynamical systems under input and state constraints. In the scenario approach, the uncertainty set is replaced by a finite set of random samples. It is proved that any feasible solution of the scenario approach will also be feasible for the original stochastic problem, although a failure risk is involved. The more the number of samples, the less failure risk will be. The main advantages of scenario approach are its generality and tractability: it converts the original stochastic problem to a deterministic convex problem regardless of the probability distribution of the uncertainty. That is, there is no need to know what the distribution is; all we need is to be able to sample from the distribution. However, to achieve a reasonable risk of failure, a large number of samples is typically needed. The scenario approach may consequently result in a computationally expensive problem since the constraints must be checked at each sample.