Efficient and Weighting Factor-Free Predictive Current Control for Three-Level PMSM Drives via Cost Function Division

Abstract

The conventional model predictive current control (MPCC) of a three-level neutral-point-clamped (3L-NPC) inverter-fed permanent magnet synchronous motor (PMSM) drive suffers from enormous computational stress due to the evaluation of comprehensive voltage vectors (VVs) in optimisation and sensitivity to manually tuned weighting factors in multi-objective cost function. This paper presents a computationally efficient MPCC algorithm that omits weighting components. The deadbeat principle directly establishes the required voltage vector (RVV), which avoids the stator current predictions, whereas a reduced control set of six VVs is derived using the angle of the RVV. The proposed cascaded technique attains current regulation and neutral-point voltage (NPV) balancing: initially, current control is achieved via six selected VVs assessed by two cost functions, thereby reducing computational load and eliminating the weighting factor. The NPV balancing control will be conditionally activated when the NPV threshold is surpassed or a small VV is selected. This control evaluates only two opposite states of selected small VV through a specified cost function. A simple optimal duty cycle technique improves current quality, whereas a unique switching frequency reduction strategy employs continuous zero VV within adjacent sampling intervals. Simulation and hardware-in-the-loop testing have confirmed the superiority of this technique compared to conventional MPCC, demonstrating reduced complexity and a substantial decrease in total harmonic distortion (THD), torque/NPV ripples, and computational burden.