New Method of Position Estimation for Self-Sensing Active Magnetic Bearings Based on Artificial Neural Network

Author

Tang, Ming ; Zhu, Changsheng

Author_Institution

Coll. of Electr. Eng., Zhejiang Univ., Hangzhou, China

fYear

2010

fDate

25-27 June 2010

Firstpage

1355

Lastpage

1358

Abstract

Artificial neural network (ANN) - as general tools for implementing nonlinear mapping between inputs and outputs - is proved to be feasible position estimation for self-sensing active magnetic bearings. A neural network with five neurons in hidden layer is constructed and well trained. With neural network act as position feedback, the active magnetic bearings system performed well. Simulation results show that neural networks can well extract the information of rotor position from the current wave form.

Keywords

backpropagation; electrical engineering computing; feedback; magnetic bearings; multilayer perceptrons; position measurement; rotors; artificial neural network; current waveform; error back propagation; mutilayer perceptron; nonlinear mapping; position estimation; position feedback; rotor position; self sensing active magnetic bearing; Artificial neural networks; Coils; Estimation; Magnetic levitation; Mathematical model; Neurons; Rotors; Active Magnetic Bearings; Artificial Neural Networks; Error back-propagation; Muti-layer perceptron; Self-sensing;

fLanguage

English

Publisher

ieee

Conference_Titel

Electrical and Control Engineering (ICECE), 2010 International Conference on

Conference_Location

Wuhan

Print_ISBN

978-1-4244-6880-5

Type

conf

DOI

10.1109/iCECE.2010.336

Filename

5630488