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 standard linear systems theory. The Koopman operator has emerged as a principled linear embedding of nonlinear dynamics, and its eigenfunctions establish intrinsic coordinates along which the dynamics behave linearly. In this work, we demonstrate a data-driven control architecture, termed Koopman Reduced Order Nonlinear Identification and Control (KRONIC), that utilizes Koopman eigenfunctions to manipulate nonlinear systems using linear systems theory. We approximate these eigenfunctions with data-driven regression and power series expansions, based on the partial differential equation governing the infinitesimal generator of the Koopman operator. Although previous regression-based methods have been plagued by spurious dynamics, we show that lightly damped eigenfunctions may be faithfully extracted using sparse regression. These lightly damped eigenfunctions are particularly relevant for control, as they correspond to nearly conserved quantities that are associated with persistent dynamics, such as the Hamiltonian. We derive the form of control in these intrinsic eigenfunction coordinates and design nonlinear controllers using standard linear optimal control theory. KRONIC is then demonstrated on a number of relevant examples, including 1) a nonlinear system with a known linear embedding, 2) a variety of Hamiltonian systems, and 3) a high-dimensional double-gyre model for ocean mixing.