Data-driven techniques for modeling, control, and sensor placement
Eurika Kaiser, University of Washington                                                                                                                                                                              


Nonlinear actuation mechanisms play a critical role for developing effective closed-loop control strategies. The probabilistic perspective of dynamical systems is particularly appealing as it yields a linear model taking into account nonlinear actuation mechanisms, and thus allows one to make use of the rich linear control theory to guarantee robust performance despite model uncertainty and external disturbances.The talk will describe a data-driven reduced-order modeling approach in which the high-dimensional flow state is coarse-grained using cluster analysis, its extension for control, and sensor placement to enable future real-time applications. Results are shown for various fluid dynamical systems.