Permanent magnet synchronous motor modeling and control

Author

Hung-Yuan Chung ; Shen-liang Lo

Author_Institution

Dept. of Electr. Eng., Nat. Central Univ., Zhongli, Taiwan

fYear

2014

Firstpage

65

Lastpage

70

Abstract

The objective of this study is to achieve permanent magnet synchronous motor (PMSM) speed control in PSIM using a self-tuning PI controller with dual feedforward artificial neural network (DFNN). The two parameters of the PI controller gain, kp and ki, can be controlled online by selftuning in the motor control system verification. The speed response, robustness of anti-load disturbance performance are better compared to a single feedforward artificial neural network (SFNN).

Keywords

PI control; adaptive control; angular velocity control; feedforward neural nets; machine vector control; neurocontrollers; permanent magnet motors; self-adjusting systems; synchronous motors; DFNN; PI controller gain; PMSM speed control; PSIM; anti load disturbance performance robustness; dual feedforward artificial neural network; field-oriented-control; ki parameter; kp parameter; motor control system verification; permanent magnet synchronous motor modeling; permanent magnet synchronous motor speed control; self- tuning; self-tuning PI controller; speed response; Artificial neural networks; Control systems; Feedforward neural networks; Fuzzy control; Mathematical model; Permanent magnet motors; Torque; Feedforward artificial neural networks; Field-oriented-control; MSM; Selftuning PI controller;

fLanguage

English

Publisher

ieee

Conference_Titel

Fuzzy Theory and Its Applications (iFUZZY), 2014 International Conference on

Print_ISBN

978-1-4799-4590-0

Type

conf

DOI

10.1109/iFUZZY.2014.7091234

Filename

7091234