A new model predictive current control strategy for PMSM based on extended control set and dynamic cost function

Finite control set model predictive control (FCS-MPC) is a commonly used control strategy for permanent magnet synchronous motor (PMSM) due to its lack of overshoot and simple principle. The precision of speed and current control is restricted in this approach due to limitations in selecting the optimal voltage vector from a finite control set consisting of only eight predetermined voltage vectors. To address these limitations and improve adaptability to variable operating conditions, this paper proposes an extended control set model predictive control (ECS-MPC) strategy for PMSM based on a dynamic cost function. Firstly, to generate more suitable voltage vectors comparing with FCS, more voltage vectors are extended in the ECS. Minimizing the error between output voltage vector and demanded voltage vector can reduce the current and speed ripples. Secondly, to adapt the strategy to changeable working conditions, a dynamic cost function based on fuzzy logical controller is designed. By dynamically adjusting the weighting factors of the cost function, the switching times can be reduced without compromising control performance. Simulation and experimental results demonstrate that the proposed algorithms enhance anti-interface performance, optimize speed and current response, and reduce the switching times of the inverter.