Model Predictive Control for PMSM Based on Discrete Space Vector Modulation with RLS Parameter Identification

被引：12
|
作者：
Yu, Hao [1 ]
Wang, Jiajun [1 ]
Xin, Zhuangzhuang [1 ]
机构：
[1] Hangzhou Dianzi Univ, Sch Automat, Hangzhou 310018, Peoples R China
来源：
ENERGIES | 2022年 / 15卷 / 11期
基金：
中国国家自然科学基金;
关键词：
permanent magnet synchronous motor; model predictive control; discrete space vector modulation; recursive least squares method; online parameter identification; DIRECT TORQUE CONTROL; MAGNET SYNCHRONOUS MOTORS; MINIMIZATION; CONVERTERS; MACHINES; RIPPLE; SET;
D O I：
10.3390/en15114041
中图分类号：
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号：
0807 ; 0820 ;
摘要：
Model Predictive Control (MPC) based on Discrete Space Vector Modulation (DSVM) has the advantages of simple mathematical model and fast dynamic response. It is widely used in permanent magnet synchronous motor (PMSM). Additionally, the control performance of DSVM-MPC is influenced by the accuracy of motor parameters and the select speed of optimal voltage vector. In order to identify motor parameters accurately, model predictive control for PMSM based on discrete space vector modulation with recursive least squares (RLS) parameter identification is proposed in this paper. Additionally, a method to preselect candidate voltage vectors is proposed to select the optimal voltage vector more quickly. The simulation model of RLS-DSVM-MPC is established to simulate the influence of different parameters on PMSM performance. The simulation results show that model predictive control for PMSM based on discrete space vector modulation with RLS parameter identification has a better control performance than that of without RLS parameter identification.
引用
收藏
页数：16
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