Learning to control switching nonlinear systems with Koopman operator regression
arXiv:2607.11344
The paper proposes using Koopman operator regression in a reproducing kernel Hilbert space to identify and control nonlinear systems with finite action spaces, creating a linear switching model that is then used in model predictive control with theoretical learning rate and sub-optimality analyses, demonstrated on a Duffing oscillator.
Abstract
In this work, we consider the identification and control of nonlinear systems with finite action spaces. The unknown dynamics are estimated from finite samples with Koopman operator regression in a reproducing kernel Hilbert space, yielding a linear switching predictive model, the switches governed by the value of the control variable. In order to perform control in closed-loop, the learned dynamics are employed in an infinite-horizon optimal control problem with time-varying stage cost, which is solved by means of model predictive control. In a theoretical analysis, we derive learning rates for the Koopman dynamics approximation. We further quantify, under suitable assumptions, the sub-optimality of the model predictive control strategy, both in the case of exact Koopman dynamics, and in the case of learned ones. Numerical simulations on the Duffing oscillator complement our theoretical findings.