SafEDMD: A Koopman-based data-driven controller design framework for nonlinear dynamical systems

Understand

The Koopman operator serves as the theoretical backbone for machine learning of dynamical control systems, where the operator is heuristically approximated by extended dynamic mode decomposition (EDMD).

• In this paper, we propose SafEDMD, a novel stability- and feedback-oriented EDMD-based controller design framework.
• Our approach leverages a reliable surrogate model generated in a data-driven fashion in order to provide closed-loop guarantees.
• In particular, we establish a controller design based on semi-definite programming with guaranteed stabilization of the underlying nonlinear system.