Schreck, G.G.SchreckThiele, G.G.ThieleFey, A.A.FeyKrüger, J.J.Krüger2022-03-142022-03-142020https://publica.fraunhofer.de/handle/publica/41116210.1109/IECON43393.2020.92546262-s2.0-85097797041For 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.en658670conference paper