Data-driven discovery of Koopman eigenfunctions for control
Data-driven discovery of Koopman eigenfunctions for control
Abstract Data-driven transformations that reformulate nonlinear systems in a linear framework have the potential to enable the prediction, estimation, and control of strongly nonlinear dynamics using linear systems theory. The Koopman operator has emerged as a principled linear embedding of nonlinear dynamics, and its eigenfunctions establish intrinsic coordinates along which …