Koopman-Nemytskii Operator: A Linear Representation of Nonlinear Controlled Systems

Abstract

While Koopman operator lifts a nonlinear system into an infinite-dimensional function space and represents it as a linear dynamics, its definition is restricted to autonomous systems, i.e., does not incorporate inputs or disturbances. To the end of designing state-feedback controllers, the existing extensions of Koopman operator, which only account for the effect of open-loop values of inputs, does not involve feedback laws on closed-loop systems. Hence, in order to generically represent any nonlinear controlled dynamics linearly, this paper proposes a Koopman-Nemytskii operator, defined as a linear mapping from a product reproducing kernel Hilbert space (RKHS) of states and feedback laws to an RKHS of states. Using the equivalence between RKHS and Sobolev-Hilbert spaces under certain regularity conditions on the dynamics and kernel selection, this operator is well-defined. Its data-based approximation, which follows a kernel extended dynamic mode decomposition (kernel EDMD) approach, have established errors in single-step and multi-step state predictions as well as accumulated cost under control.