LAUSR

This paper proposes a novel deadbeat current control (DBCC)-based model predictive control for an asymmetrical six-phase permanent magnet synchronous machine. First, the solution of DBCC is adopted to obtain the expected reference voltage vector (RVV). Then, two groups of virtual vectors, in the total number of 24 with different magnitudes, are defined for the sake of current harmonics suppression. Subsequently, two in-phase virtual vectors which are closest to the RVV are selected as the prediction vectors. The next step is to define a cost function which is composed of the error between the RVV and the available prediction vectors. Then, the selected two virtual vectors are evaluated and the one that minimizes the cost function will be applied in the next instant. In this way, only two prediction vectors need to be evaluated and the computation burden is highly alleviated. In the meantime, the weighting factor involved in predictive torque control is avoided. In addition, to achieve the readily implementation with standard pulsewidth modulation switching sequence, 18 virtual vectors are artfully replaced by their corresponding equivalent virtual vectors. Finally, the proposed method is comparatively studied and compared with other benchmark methods. Simulation and experimental results are offered to confirm the effectiveness of the proposed method.