Direct-vector finite set current predictive control strategy eliminating exhaustive voltage vector evaluation for SynRM drives
Abstract
This paper investigates a direct vector finite-set current predictive control (DV-FSCPC) strategy for a two-level inverter–driven synchronous reluctance motor (SynRM). The main contribution of this work lies in the development of simplified FSCPC strategies that progressively reduce the number of evaluated voltage vectors from eight to three, two, and finally to one vector. To further improve speed regulation, the Artificial Protozoa Optimizer (APO) is utilized to optimally tune the gains of the proportional–integral speed controller. The proposed control strategy is implemented in MATLAB/Simulink and examined under various operating conditions. The obtained simulation results indicate that the proposed technique achieves a noticeable reduction in computational effort faster than the conventional methods, which is reflected in the execution time of the control algorithm that directly determines the optimal switching vector without requiring exhaustive evaluation. Specifically, the conventional eight-vector scheme requires 0.232 s, whereas the execution time decreases to 0.216 s with the three-vector configuration and further drops to 0.212 s when the two-vector strategy is applied. When the one-vector strategy is employed, the execution time is further reduced to 0.072 s. The results clearly show that using the direct evaluation approach can reduce computational time by up to 69% compared to the conventional full-state method. Overall, the results demonstrate that the proposed DV-FSCPC offers an efficient balance between control performance and computational efficiency while ensuring reliable and robust operation of the SynRM drive system.