Generalized Multiple-Vector-Based Model Predictive Control for PMSM Drives

Abstract

Model predictive control (MPC) is emerging as a powerful control method for the high performance control of permanent magnet synchronous motor (PMSM) drives due to its merits of simple principle, quick response, and flexibility to handle multiple variables and constraints. However, conventional MPC applies only one voltage vector during one control period to minimize the cost function, which produces relatively high steady-state ripples and high computational burden due to the enumeration-based predictions. Introducing duty cycle control into MPC can improve its steady-state performance, but the control complexity is further increased. This paper proposes a generalized multiple-vector-based MPC for PMSM drives, which unifies the prior MPC methods in one frame with much lower complexity and computational burden by eliminating the enumeration-based predictions and complex calculations in conventional MPC methods. This is achieved by reconstructing the three-phase duties obtained from the classical deadbeat control with modulator, which also reveals the inherent relationship between deadbeat control and the proposed MPC methods. The presented experimental results confirm the effectiveness of the proposed method.