A Multivector-Based Model Predictive Current Control of PMSM Drive With Enhanced Torque and Flux Response

Abstract

Model predictive current control (MPCC) is a modern current control technique with the provision to include multivariable objectives and nonlinearities. In this article, a multivector-based MPCC is proposed for the speed control of a two-level voltage source inverter (VSI) fed permanent magnet synchronous motor (PMSM) drive, to improve the steady-state (SS) torque and flux response. The multivector operation is obtained using an extended control set (ECS) that utilizes two adjacent-active voltage-space vectors (VSVs) with an appropriate distribution coefficient. However, the augmentation of the control set (CS) catalyzes an undesirable increase in the computational burden. To address this limitation, a lookup table-based VSV preselection scheme based on stator current error is employed. The VSV grouping is based on the gradient of stator current error and effectively replicates the control scheme with all ECS-VSVs. Subsequently, the amplitude of the selected optimum ECS-VSV is optimized using an average error minimization technique. The optimum duration of the ECS-VSV is determined without employing space vector modulation (SVM). Hence, the complexity of the proposed scheme is minimized, and the SS torque and flux performance are enhanced without compromising the dynamic performance of the drive. The effectiveness of the proposed scheme is verified by conducting comprehensive comparisons with conventional MPCC and recently published literature.