  Predictive control of power electronic systems always requires a suitable
model of the plant. Using typical physics-based white box models, a trade-off
between model complexity (i.e. accuracy) and computational burden has to be
made. This is a challenging task with a lot of constraints, since the model
order is directly linked to the number of system states. Even though white-box
models show suitable performance in most cases, parasitic real-world effects
often cannot be modeled satisfactorily with an expedient computational load.
Hence, a Koopman operator-based model reduction technique is presented which
directly links the control action to the system's outputs in a black-box
fashion. The Koopman operator is a linear but infinite-dimensional operator
describing the dynamics of observables of nonlinear autonomous dynamical
systems which can be nicely applied to the switching principle of power
electronic devices. Following this data-driven approach, the model order and
the number of system states are decoupled which allows us to consider more
complex systems. Extensive experimental tests with an automotive-type permanent
magnet synchronous motor fed by an IGBT 2-level inverter prove the feasibility
of the proposed modeling technique in a finite-set model predictive control
application.
