Predictive Control with Reduced Computational Burden in Cascaded H-Bridge Motor Drives

This paper presents a predictive control method for a Permanent Magnet Synchronous Motor (PMSM) driven by a seven-level Cascaded H-Bridge inverter. The control is based on a Tree-Based Model-Free Predictive Control and an online calculation of reduced Look-Up Tables. The Tree-Based approach selects the optimal Voltage Vector, reducing the number of tested vectors. The online LUTs handle the generation of gate signals, ensuring online battery balancing by selecting the appropriate switching state. This method reduces the controller's computational effort, improves torque control, lowers switching losses, and manages the battery State of Charge. Additionally, the proposed control technique eliminates the dependency on a load model by introducing a Model-Free approach, based on a recursive least squares (RLS) algorithm to identify the parameters of an Auto-Regressive with Exogenous input (ARX) model. Simulation results confirm the effectiveness of the proposed control for accurate motor control, reduced switching frequency losses, and SOC management.