Improved Model Predictive Current Control for Linear Vernier Permanent-Magnet Motor With Efficient Voltage Vectors Selection
Chen Wang, Jinghua Ji, Hongyu Tang, Tao Tao, Wenxiang Zhao
Abstract
In order to relieve the computational burden and reduce the ripples in thrust force and current of linear vernier permanent-magnet motors, this article proposes three-vector-based model predictive current control (MPCC) with efficient voltage vectors selection. The proposed method avoids the traversal of all the possible voltage vectors. Only three nonadjacent active voltage vectors are predicted and evaluated by the cost function. Two active voltage vectors can be precisely determined according to the relationship of three cost function values. The prediction workload can be reduced with the efficient voltage vectors selection. Also, the two active voltage vectors along with the null voltage vector are used to synthesize the final applied voltage vector based on deadbeat principle. The proposed three-vector-based MPCC method extends the modulation area from discrete points to the whole regular hexagon. Thus, the ripples in thrust force and current can be minimized. Theoretical analyses and experimental results are given to verify the effectiveness of the proposed MPCC.