Virtual Three-Level Model Predictive Flux Control With Reduced Computational Burden and Switching Frequency for Induction Motors

Abstract

Model predictive flux control (MPFC) is an alternative control solution to the classic model predictive torque control (MPTC) for driving induction motors (IMs) without the complexity of tuning the weighting factors. However, when applied in motor drives fed by a two-level converter, it results in higher flux and torque ripples. To improve its performance, this article proposes a virtual three-level MPFC (V3-MPFC) method. The virtual vector method increases the total number of voltage vectors crucial for improving the closed-loop performances of the model predictive control. To reduce the computational burden, direct torque control and novel deadbeat (DB) control strategies are proposed. Compared with existing methods, the proposed DB-based method reduces V3-MPFC to a suboptimization problem and still ensures that the suboptimal solution is equivalent to the global optimal solution provided by V3-MPFC. In addition, the redundant states are exploited to lower the switching frequency (SF) without extending the cost function. The simulation and experiment results show that, compared with existing methods, the proposed strategy effectively reduces the flux and torque ripples, and requires a lower SF and execution time.