  A data set was recorded to evaluate different methods for extracting
mathematical models for a three-phase permanent magnet synchronous motor (PMSM)
and a two-level IGBT inverter from measurement data. It consists of
approximately 40 million multidimensional samples from a defined operating
range of the drive. This document describes how to use the published data set
\cite{Dataset} and how to extract models using introductory examples. The
examples are based on known ordinary differential equations, the least squares
method or on (deep) machine learning methods. The extracted models are used for
the prediction of system states in a model predictive control (MPC) environment
of the drive. In case of model deviations, the performance utilizing MPC
remains below its potential. This is the case for state-of-the-art white-box
models that are based only on nominal drive parameters and are valid in only
limited operation regions. Moreover, many parasitic effects (e.g. from the
feeding inverter) are normally not covered in white-box models. In order to
achieve a high control performance, it is necessary to use models that cover
the motor behavior in all operating points sufficiently well.
