Computationally Efficient Finite-Set Model Predictive Current Control of Interior Permanent Magnet Synchronous Motors with Model-Based Online Inductance Estimation

被引：0

作者：

Hammoud, Issa ^{[1
,2
]}

Hentzelt, Sebastian ^{[2
]}

Oehlschlaegel, Thimo ^{[2
]}

Kennel, Ralph ^{[1
]}

机构：

[1] Tech Univ Munich TUM, Inst Elect Drive Syst & Power Elect, Munich, Germany

[2] IAV GmbH, Powertrain Mechatron Control Engn Excellence Clus, Gifhorn, Germany

来源：

2019 IEEE CONFERENCE ON POWER ELECTRONICS AND RENEWABLE ENERGY (IEEE CPERE) | 2019年

关键词：

Model predictive current control; model-based inductance estimation; interior permenant magnet synchronus motor; online optimization; electrical drives;

D O I：

10.1109/cpere45374.2019.8980058

中图分类号：

X [环境科学、安全科学];

学科分类号：

08 ; 0830 ;

摘要：

In this paper, solutions to the main drawbacks of the traditional finite-set model predictive current control (FS-MPCC) of interior permanent magnet synchronous motors (IPMSM) are proposed. These drawbacks are: high computational load and high sensitivity to any model mismatch. The proposed computationally efficient FS-MPCC is based on finding the optimal voltage vector (VV) that would enhance the flow of the desired currents analytically. Based on its location in the stationary alpha-beta plane, only three iterations of a modified cost function will be needed. Furthermore, a novel model-based online inductance estimation technique is proposed to enhance the robustness of the controller against model mismatch.

引用

页码：290 / 295

页数：6