Low Complexity Zero-Suboptimal Model Predictive Torque Control for SPMSM Drives Based on Discrete Space Vector Modulation

The torque and stator flux control performance of surface permanent magnet synchronous motor (SPMSM) drives based on finite control set model predictive torque control (FCS-MPTC) can be improved by the discrete space vector modulation (DSVM) technique. However, enumerating all candidate voltage vectors (CVVs) during the prediction and evaluation process significantly increases the computational burden for the DSVM-MPTC algorithm. Therefore, this article proposes a zero-suboptimal model predictive torque control (ZS-MPTC) to reduce the computational burden and realize the dual-objective optimal control of torque and stator flux for SPMSM drives based on DSVM. In this method, the enumeration number of CVVs is adjusted according to the real-time change of reference voltage vector under different operating conditions. Compared with traditional DSVM-MPTC, in which 38 CVVs need to be enumerated in the cost function, only 2, 4, or 6 CVVs are evaluated to determine the global optimal voltage vector based on the different amplitudes of reference voltage vector in the proposed method. Compared with existing methods, the proposed method has the characteristic of high search efficiency of the global optimal voltage vector. In addition, the dynamic and steady-state zero-suboptimal control of dual-objective DSVM-MPTC is realized. The experimental results validate the effectiveness of the proposed method.