Intelligent control of alternative current permanent magnet servomotor using neural network

Author

Zhifei, Chen ; Yuejun, An ; Kebing, Jia ; Changzhi, Sun

Author_Institution

Sch. of Electr. Eng., Shenyang Univ. of Technol., China

Volume

2

fYear

2001

fDate

37104

Firstpage

743

Abstract

A momentum back-propagation neural network is applied to control of alternative current permanent magnet servo-motors operating in a high performance drives environment to inhibit the network from converging at the local minimum point. The emerging backpropagation neural networks have the capability to learn arbitrary nonlinearity and show great potential for intelligent control applications. A scheme for combing momentum back-propagation neural networks with traditional PID control techniques is proposed. The control action emulated with the aid of MATLAB shows that the new self-turning scheme can deal with a large unknown non-linearity

Keywords

AC motors; backpropagation; control system analysis computing; electric machine analysis computing; machine control; machine theory; neurocontrollers; permanent magnet motors; servomotors; AC permanent magnet servomotor; MATLAB; PID control techniques; computer simulation; control design; control simulation; momentum back-propagation neural network; neural network-based control; Artificial neural networks; Biological neural networks; Computer networks; Intelligent control; Mathematical model; Neural networks; Neurons; Permanent magnets; Three-term control; Transfer functions;

fLanguage

English

Publisher

ieee

Conference_Titel

Electrical Machines and Systems, 2001. ICEMS 2001. Proceedings of the Fifth International Conference on

Conference_Location

Shenyang

Print_ISBN

7-5062-5115-9

Type

conf

DOI

10.1109/ICEMS.2001.971783

Filename

971783