Model Predictive Current Control of Permanent Magnet Synchronous Linear Motor Based on Expanded Control Set

Finite control set model predictive current control (FCS-MPCC) often induces notable thrust fluctuations and elevated current harmonics in permanent magnet synchronous linear motor (PMSLM) drives. These issues primarily arise due to the limited number of available voltage vectors, which compromise steady-state accuracy. To address this problem, a model predictive current control strategy utilizing an expanded control set is proposed. In this approach, virtual voltage vectors are synthesized from the fundamental voltage vectors, expanding the control set to include 38 candidate vectors. Using current prediction equations and a value function, the optimal candidate region is identified by preselecting a central voltage vector, which significantly reduces the number of required optimization searches. The voltage vectors within this region are evaluated at two distinct prediction times. After analyzing the results from both moments, the voltage vector that yields the smallest current prediction error is selected as the optimal control input. A simulation analysis is conducted to validate the effectiveness of the proposed method. The results demonstrate that, compared to conventional FCS-MPCC, the expanded control set approach significantly reduces current harmonics and thrust fluctuations, while also mitigating the increase in switching frequency typically associated with a larger control set.