  The performance of model predictive controllers (MPC) strongly depends on the
model quality. In the field of electric drive control, white-box (WB) modeling
approaches derived from first-order physical principles are most common. This
procedure typically does not cover parasitic effects and parameter deviations
are frequent. These issues are particularly crucial in the domain of
self-commissioning drives when a hand-tailored, accurate WB plant model is not
available. In order to compensate for such modeling errors and, therefore, to
improve the control performance during transients and steady-state, this paper
proposes a data-driven, real-time capable recursive least squares (RLS)
estimation method for the current control of a permanent magnet synchronous
motor (PMSM). The effect of the flux linkage and voltage harmonics due to the
winding scheme can also be taken into account. Moreover, a compensating scheme
for the interlocking time of the inverter is proposed. The proposed
identification algorithm is investigated using the well-known
finite-control-set MPC (FCS-MPC) in the rotor-oriented coordinate system. The
extensive experimental results show the superior performance of the presented
scheme compared to a FCS-MPC-based on a state-of-the-art WB motor model using
look-up tables for adressing (cross-)saturation.
