Finite Control Set-Model Predictive control of Permanent Magnet Synchronous Motor

Abstract: The electrification of the transportation sector has been the most important goal of the current century. In the last decade, there has been a surge in the production and sale of electric vehicles, necessitating substantial research and development in the industry. This thesis focuses on building a FiniteControl Set Model Predictive controller(FCS-MPC) for a Permanent Magnet Synchronous Motor(PMSM) drive in a back-to-back setup, at China Euro Vehicle Technology (CEVT). A conventional 8 vector based FCS-MPC with a multi-objective cost function was designed. The cost consisted of current tracking and torque tracking components along with other objectives aimed at reducing the ripples and switching losses to improve the performance of the controller. A modified FCS-MPC algorithm with a set consisting of 50 voltage vectors was designed to improve the performance of the FCS-MPC at steady state. An efficient search algorithm was designed for the modified FCS-MPC in order to utilize the same computational power as the conventional FCS-MPC. As a result of this search algorithm, irrespective of the 50 vectors in the set only 8 vectors are used for the prediction process for every cycle. To address the issue of parameter mismatch, the MPC was enhanced with an online disturbance observer. Finally, simulation was used to compare the devised control algorithm to field oriented control (FOC). When comparing the FOC to the FCS-MPC, the results showed that the FOC had less ripple and better steady-state performance. The FCS-MPC, on the other hand, had better transient performance, with a shorter settling time and less overshoot. When compared to FOC, the FCS-MPC’s transient performance was considerably better under parameter mismatch.