February 15, 2019, Webb 1100

Traditional sample-based uncertainty propagation methods are generally computationally expensive for online optimization applications. In this talk, we will discuss the use of arbitrary polynomial chaos (aPC) for quantification of probabilistic uncertainties with arbitrary measures (e.g., uncertainties with correlated multivariate or multi-modal distributions). We show how aPC can be used to obtain efficient surrogate models for optimization-based analysis, estimation, and control of nonlinear systems with probabilistic uncertainties. In particular, we will demonstrate the application of aPC-based surrogate models for: (1) the design and performance verification of model predictive controllers for stochastic nonlinear systems, and (2) optimal Bayesian experiment design.

Ali Mesbah is Assistant Professor of Chemical and Biomolecular Engineering at the University of California at Berkeley. Before joining UC Berkeley, he was a senior postdoctoral associate at MIT. He holds a Ph.D. degree in systems and control from Delft University of Technology. He is a senior member of the IEEE Control Systems Society and AIChE. He is on the IEEE Control Systems Society conference editorial board as well asthe editorial board of IEEE Transactions on Radiation and Plasma Medical Sciences. He is the recipient of the AIChE's 35 Under 35 Award in 2017, the IEEE Control Systems Outstanding Paper Award in 2017, and the AIChE CAST W. David Smith, Jr. Graduation PublicationAward in 2015. His research interests are in the areas of optimization-based systems analysis, fault diagnosis, and predictive control of uncertain systems.