Koopman-Based Robust Model Predictive Control with Online Identification for Nonlinear Dynamical Systems

Abstract

Dear Editor, This letter presents a novel approach to the data-driven control of unknown nonlinear systems. By leveraging online sparse identification based on the Koopman operator, a high-dimensional linear system model approximating the actual system is obtained online. The upper bound of the discrepancy between the identified model and the actual system is estimated using real-time prediction error, which is then utilized in the design of a tube-based robust model predictive controller. The effectiveness of the proposed approach is validated by numerical simulation.