Novel Model Predictive Control Strategies for PMSM Drives: Reducing Computational Burden and Enhancing Real-Time Implementation

Model predictive control (MPC) has emerged as a favorable control approach for PMSM drives, though its practical deployment is frequently hindered by superior computational complexity and execution burden. This paper presents four finite control set MPC (FCS-MPC) techniques applied to a two-level inverter-fed PMSM drive. Two of the approaches are conventional methods, while the other two are novel developed strategies proposed in this paper. The novel techniques focus on significantly decreasing computational burdens by employing an efficient space-vector selection mechanism that quickly selects the optimum switching vector without exhaustive evaluation. A comprehensive comparative assessment of all four control methods is conducted under various operating conditions, evaluating their dynamic and steady-state performance, computational requirements, and real-time feasibility. Simulation results demonstrate that the proposed techniques achieve a significant reduction in computational effort and faster processing, up to 39.65% faster than conventional full-state evaluation, while maintaining control performances comparable to conventional techniques. These results highlight the potential of the proposed MPC approaches to bridge the gap between advanced control theory and practical implementation in real-time PMSM drive systems, providing effective solutions for installing high-performance PMSM drives on hardware with limited resources.