Control of brushless DC motor based on fuzzy rules optimized by genetic algorithm used in hybrid vehicle

被引：3

作者：

Niu, Xiaoyan ^{[1
]}

Feng, Guosheng ^{[1
]}

Jia, Sumei ^{[1
]}

Zhang, Yuquan ^{[2
]}

机构：

[1] Shijiazhuang Tiedao Univ, Sch Mech Engn, Shijiazhuang 050043, Hebei, Peoples R China

[2] Heibei Jiaotong Vocat & Tech Coll, Shijiazhuang 050035, Hebei, Peoples R China

来源：

JOURNAL OF COMPUTATIONAL METHODS IN SCIENCES AND ENGINEERING | 2021年 / 21卷 / 04期

关键词：

Hybrid; brushless DC motor (BLDCM); speed regulation; fuzzy control; genetic algorithm; dSPACE;

D O I：

10.3233/JCM-204628

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

To improve the control precision of the closed-loop speed regulation of the brushless DC motor (Brushless DC motor, BLDCM) used in hybrid vehicles, the paper established a fuzzy controller for closed loop control of speed, taking the speed difference and the conversion rate of the speed difference as the inputs, and the increment of the supply voltage as the output, and proposed a method of optimizing fuzzy control rules based on genetic algorithm. Established the mathematical model of BLDCM in MATLAB/Simulink, simulated the traditional PID controller, the fuzzy controller and the fuzzy controller optimized by genetic algorithm. Then the hardware-in-the-loop motor control system is constructed based on dSPACE. The simulation and experiment prove that the optimization of fuzzy rules by genetic algorithm is an effective method to improve the accuracy of speed control, and it has good tracking performance for frequently changing speed requirements in hybrid vehicle.

引用

页码：951 / 968

页数：18

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