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abstract: 'Hysteresis-controlled devices are widely used in industrial applications. For example, cooling devices usually contain a two-point controller, resulting in a nonlinear hybrid system with two discrete states. Dynamic models of systems are essential for optimizing such industrial supply technology. However, conventional system identification approaches cannot handle hysteresis-controlled devices. Thus, the new identification method Sparse Identification of Nonlinear Dynamics (SINDy) is extended to handle hybrid systems. In this new method (SINDyHybrid), tailored basis functions in form of relay hysterons are added to the library which is used by SINDy. Experiments with a hysteresis controlled water basin show that this approach correctly identifies state transitions of hybrid systems and also succeeds in modeling the dynamics of the discrete system states. A novel proximity hysteron achieves the robustness of this method. The impacts of the sampling rate and the signal noise ration of the measurement data are examined accordingly.'
author:
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bibliography:
- 'IEEEabrv.bib'
- 'literature.bib'
title: 'System identification of a hysteresis-controlled chiller plant using SINDy [^1] '
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SINDyHybrid, system identification, nonlinear systems, hysteresis, hybrid dynamical systems, sparse regression

Introduction
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Nowadays, data-driven approaches for modeling dynamics without a fixed model structure gain relevance. However, classical grey-box modeling is 