Open AccessThree‐vector‐based low‐complexity model predictive current control with reduced steady‐state current error for permanent magnet synchronous motor
Author(s) -
Xu Yanping,
Ding Xianhua,
Wang Jibing,
Li Yuanyuan
Publication year - 2020
Publication title -
iet electric power applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.815
H-Index - 97
eISSN - 1751-8679
pISSN - 1751-8660
DOI - 10.1049/iet-epa.2019.0108
Subject(s) - control theory (sociology) , current (fluid) , model predictive control , steady state (chemistry) , ripple , observer (physics) , sensitivity (control systems) , compensation (psychology) , current loop , torque , voltage , computer science , engineering , control (management) , physics , electronic engineering , chemistry , electrical engineering , artificial intelligence , psychology , quantum mechanics , psychoanalysis , thermodynamics
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.