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Optimal construction of Koopman eigenfunctions for prediction and control
Milan Korda, Igor Mezić
This work presents a novel data-driven framework for constructing eigenfunctions of the Koopman operator geared toward prediction and control. The method leverages the richness of…
Convex computation of extremal invariant measures of nonlinear dynamical systems and Markov processes
Milan Korda, Didier Henrion, Igor Mezic
We propose a convex-optimization-based framework for computation of invariant measures of polynomial dynamical systems and Markov processes, in discrete and continuous time. The se…
Moments and convex optimization for analysis and control of nonlinear partial differential equations
Milan Korda, Didier Henrion, Jean-Bernard Lasserre
This work presents a convex-optimization-based framework for analysis and control of nonlinear partial differential equations. The approach uses a particular weak embedding of the…
A data-driven Koopman model predictive control framework for nonlinear flows
Hassan Arbabi, Milan Korda, Igor Mezic
The Koopman operator theory is an increasingly popular formalism of dynamical systems theory which enables analysis and prediction of the nonlinear dynamics from measurement data.…
Power grid transient stabilization using Koopman model predictive control
Milan Korda, Yoshihiko Susuki, Igor Mezić
This work addresses the problem of transient stabilization of a power grid, following a destabilizing disturbance. The model considered is the cascade interconnection of seven New…