3108 Etcheverry Hall
Department of Chemical and Biomolecular Engineering, University of California Berkeley
Abstract: Traditional sample-based uncertainty propagation methods are generally computationally expensive for online optimization applications. In this talk, we will discuss arbitrary polynomial chaos (aPC) for quantification of probabilistic uncertainties with arbitrary measures (e.g., uncertainties with correlated multivariate or multi-modal distributions). aPC can be used as an efficient uncertainty propagation method for optimization-based analysis, estimation, and control of nonlinear systems with probabilistic uncertainties In particular, we will demonstrate the use of aPC for the design and performance verification of model predictive control (MPC) for stochastic nonlinear systems.
Research interests: Our research lies at the intersection of control theory, applied mathematics, and process systems engineering. The main thrust of our theoretical research is to develop novel systems analysis techniques and application-relevant control theory for complex dynamical systems that are stochastic and nonlinear. The systems analysis and control theory developments are intended to (i) improve our fundamental understanding of complex chemical and biological systems in order to answer specific questions related to underlying physicochemical or biological mechanisms of a system, and (ii) enable high-performance and cost-effective control of complex systems using physics-based knowledge of their dynamics. Our multidisciplinary research efforts provide a balance between theory, computation, and real-word applications, with a particular emphasis on energy and life science applications.