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Proceedings Paper

Sensorless control for permanent magnet synchronous motor using a neural network based adaptive estimator
Author(s): Chung-Jin Kwon; Sung-Joong Kim; Woo-Young Han; Won-Kyoung Min
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Paper Abstract

The rotor position and speed estimation of permanent-magnet synchronous motor(PMSM) was dealt with. By measuring the phase voltages and currents of the PMSM drive, two diagonally recurrent neural network(DRNN) based observers, a neural current observer and a neural velocity observer were developed. DRNN which has self-feedback of the hidden neurons ensures that the outputs of DRNN contain the whole past information of the system even if the inputs of DRNN are only the present states and inputs of the system. Thus the structure of DRNN may be simpler than that of feedforward and fully recurrent neural networks. If the backpropagation method was used for the training of the DRNN the problem of slow convergence arise. In order to reduce this problem, recursive prediction error(RPE) based learning method for the DRNN was presented. The simulation results show that the proposed approach gives a good estimation of rotor speed and position, and RPE based training has requires a shorter computation time compared to backpropagation based training.

Paper Details

Date Published: 2 May 2006
PDF: 6 pages
Proc. SPIE 6042, ICMIT 2005: Control Systems and Robotics, 60422F (2 May 2006); doi: 10.1117/12.664658
Show Author Affiliations
Chung-Jin Kwon, Chonbuk Univ. (South Korea)
Sung-Joong Kim, Chonbuk Univ. (South Korea)
Woo-Young Han, Chonju Technical College (South Korea)
Won-Kyoung Min, Hanyang Univ. (South Korea)

Published in SPIE Proceedings Vol. 6042:
ICMIT 2005: Control Systems and Robotics
Yunlong Wei; Kil To Chong; Takayuki Takahashi; Shengping Liu; Zushu Li; Zhongwei Jiang; Jin Young Choi, Editor(s)

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