Discrete Space Vector Modulation-Based Model Predictive Torque Control With No Suboptimization

Abstract

This article presents a simplified discrete space vector modulation (DSVM)-based predictive torque control (PTC) scheme in order to improve the performance of a two-level inverter-fed induction motor drive. DSVM technique creates a number of virtual vectors which are evaluated in the conventional all vector-based discrete space vector modulation-based model predictive torque control (DSVM-MPTC) method. The high number of admissible vectors increases the computational burden of DSVM-MPTC, significantly. In this article, an efficient optimal voltage vector selection method is proposed to reduce the computational load of DSVM-MPTC from 37 to 13 enumerations. The vector selected from the reduced set of admissible voltage vectors produces the same cost function value as that of all vector-based DSVM-MPTC in the entire range of operation of induction motor (IM) drives. The proposed method reduces the computational burden effectively without causing any suboptimization issues in both transients and steady states. Experimental results verify the effectiveness of the proposed algorithm and its superior performance compared to the switching-table-based DSVM-MPTC and the classic finite-control-set model-predictive-control which only utilizes the real voltage vectors.