An Improved Switching-Instant-Optimization-Based Model Predictive Flux Control of PMSMs

Model predictive flux control, with its simplified cost function and multi-objective optimization, has emerged as an effective permanent magnet synchronous motor control method. The conventional method’s reference tracking performance is limited by the fixed action time for each voltage vector. This paper proposes an improved switching-instant-optimization-based model predictive flux control method to address this issue. The goal is to minimize the flux tracking error by optimizing the switching instant of adjacent voltage vectors. Control freedom is increased since the voltage vectors can be switched before or after the sampling point. The proposed method has the capability to decrease torque ripples and current harmonics in comparison to the conventional predictive flux control scheme. According to experimental findings, this algorithm can guarantee excellent transient responsiveness while enhancing the system’s steady-state performance at the same average switching frequency.