Robust system identification for hysteresis-controlled devices using SINDy

For advanced control of technical systems reliable system identification methods are essential. The data-driven framework Sparse Identification of Nonlinear Dynamics (SINDy) by Kutz and Brunton is extended in order to tackle hysteresis-controlled systems. In order to gain robustness, a so called proximity hysteron is introduced. This paper presents this extension and documents experiments with simulations of an academic example and an industrial chiller system. A proof of concept is followed by experiments which show that strong nonlinearities as well as inadequate sampling rates can critically impair the algorithm.