Three‐vector‐based low‐complexity model predictive current control with reduced steady‐state current error for permanent magnet synchronous motor
Yanping Xu, Xianhua Ding, Jibing Wang, Yuanyuan Li
Abstract
The three‐vector‐based model predictive current control has the advantages of fast dynamic response, low current ripple and no weight factor, but there are also problems of large computational efforts and steady‐state current error under parameter mismatch. To solve the fore‐mentioned drawbacks, a three‐vector‐based low‐complexity model predictive current control with reduced steady‐state current error for the permanent magnet synchronous motor drive system is proposed in this study. Firstly, the selection process of optimal voltage vector combination is simplified to reduce the computational burden of three‐vector‐based model predictive current control. Moreover, the sensitivity of parameters is analysed, respectively. In order to reduce the steady‐state current error caused by parameter mismatch, a Luenberger observer is introduced to estimate the lump disturbance caused by parameter mismatch and unmodelled dynamics. The estimated lump disturbance is considered as compensation to the model. Finally, the validity of the proposed method is verified by experiments.