Flux-Based Dynamic Subspace Model Predictive Control of Dual-Three Phase Permanent Magnet Synchronous Motors

Abstract

ual-three phase permanent magnet synchronous motors (DTP-PMSM) are becom ing more popular in the automotive field. Their potential to increase the reliability and efficiency of the vehicle makes them an attractive replacement for the three phase alternative. However, the increased number of phases makes the control of the machine more complex. As a result, conventional controllers can see reduced perfor mance, especially at high speeds and torques. Currently, with the increased process ing power of modern micro-controllers and field-programmable gate arrays (FPGA), many researchers are investigating whether finite-control set model predictive control (FCS-MPC) can be a suitable alternative. FCS-MPC is simple to implement and can achieve a better dynamic performance when compared to other controllers. Furthermore, the algorithm can be augmented for specific optimization goals and non-linearities to the system, which gives the designer creativity in improving the system response. However, Model-Predictive Control suffers from a variable switching frequency as well as reduced steady-state performance. It generally has increased current ripple in the phase currents. This thesis presents a method of reducing the steady-state ripples in FCS-MPC by introducing the use of virtual-flux in the model equations, the incremental model, and a dynamic vector search-space. All three of these applications make FCS-MPC have a iv significantly improved steady-state performance when compared to the conventional algorithm, while still keeping the benefit of the improved dynamic response. The benefits of the proposed techniques techniques are verified through simulation as well as on an experimental setup.