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

Hao Yu, Jiajun Wang and Zhuangzhuang Xin
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Hao Yu: School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China
Jiajun Wang: School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China
Zhuangzhuang Xin: School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China

Energies, 2022, vol. 15, issue 11, 1-16

Abstract: 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.

Keywords: permanent magnet synchronous motor; model predictive control; discrete space vector modulation; recursive least squares method; online parameter identification (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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