A Computationally Efficient Finite Control Set Model Predictive Control for Multiphase PMSM Drives

Abstract

Finite control set model predictive control (FCS-MPC) has been widely studied and applied in power converters and machine drives. It takes advantage of the nonlinear finite switching states of the power converters, and a discrete model is used to predict the behavior of the system. The main advantages of FCS-MPC lie in easy inclusion of nonlinear constraints, simple structure and intuitive concept. However, FCS-MPC applied in multiphase machine drives will face with the challenge of heavy computation cost. In this article, a computationally efficient FCS-MPC method is proposed for multiphase drives. The concept of virtual voltage vector is introduced to eliminate low-order harmonic currents in open-loop mode, resulting in simplified prediction model and cost function. To further simplify the enumerative optimization process, the redundant vectors are eliminated, and only five evaluations are required. By this way, the computation complexity is considerably reduced and is approximately irrespective of phase number. Thus, it can be easily generalized to multiphase drives. Moreover, the merit of simple structure is kept, which is the most important characteristic of FCS-MPC. The proposed method is compared with the existing FCS-MPC methods to illustrate its effectiveness. The experimental results have verified that the proposed method can reduce computation complexity, achieve superior steady-state and dynamic performance.