Torque and Inductances Estimation for Finite Model Predictive Control of Highly Utilized Permanent Magnet Synchronous Motors

For many permanent magnet synchronous motor (PMSM) drive applications (e.g., traction or automation), precise torque control is desired. Classically, this is based on extensive offline motor identification, e.g., by direct mapping of torque–flux–current look-up tables. In contrast, this article proposes a torque estimation method based on online differential inductances identification in combination with a data-driven finite-control-set (FCS) model predictive current control (MPCC). This scheme does not require offline identification or expert motor design knowledge. The required flux maps are determined by integrating the differential inductances in the left $i_{\mathrm{d}}$–$i_{\mathrm{q}}$ half-plane. By considering varying differential inductances, the proposed method is ideally suited for highly utilized PMSM with significant (cross-) saturation effects where estimation models with constant inductances fail. For the identification of the differential inductances, the system excitation, based on the FCS-MPCC working principle, is utilized. Consequently, no additional signal injection is required and the estimation scheme is applicable in the entire speed range. With this method, an open-loop torque control can be realized without knowledge of exact motor parameters except the permanent magnet flux linkage as a datasheet parameter. Extensive experimental investigations on a highly utilized PMSM in the entire speed range including standstill prove the performance of the proposed approach.