ELM-based sensorless speed control of permanent magnet synchronous machine

被引：0

作者：

Kumar, Vikas ^{[1
]}

Gaur, Prerna ^{[1
]}

Mittal, A.P. ^{[2
]}

Singh, Bhim ^{[3
]}

机构：

[1] Division of Instrumentation and Control Engineering, Netaji Subhas Institute of Technology (NSIT), Dwarka, New Delhi 110078, Azad Hind Fauj Marg

[2] Netaji Subhas Institute of Technology (NSIT), Dwarka, New Delhi 110078, Azad Hind Fauj Marg

[3] Electrical Engineering Department, Indian Institute of Technology (IIT) Delhi, Hauz Khas

来源：

Kumar, V. (sainivika@gmail.com) | 2013年 / Inderscience Enterprises Ltd., 29, route de Pre-Bois, Case Postale 856, CH-1215 Geneva 15, CH-1215, Switzerland卷 / 11期

关键词：

Artificial neural network; Back propagation; BP; ELM; Extreme learning machine; Permanent magnet synchronous machine; PMSM; Sensorless control;

D O I：

10.1504/IJVAS.2013.053779

中图分类号：

学科分类号：

摘要：

This paper deals with Extreme Learning Machine (ELM) based sensorless speed estimation and speed control of Permanent Magnet Synchronous Machines (PMSMs). ELM, first proposed by G.B. Huang as a new class of learning algorithm for Single-Hidden Layer Feedforward Neural Networks (SLFNs), is extremely fast and accurate, and has better generalisation performance than the traditional gradient-based training methods. To implement Field-Oriented Control (FOC) in PMSMs, the stator magnetic field is always kept 90 degrees ahead of the rotor. This requires rotor position information all the time. This information is accurately obtained with an ELM-based observer without the position sensor for PMSMs, and hence, the cost of the system is reduced, while the problems associated with the sensors are minimised. Copyright © 2013 Inderscience Enterprises Ltd.

引用

页码：190 / 204

页数：14

共 20 条

[1]

Ampazis N., Perantonis S.J., Two highly efficient second-order algorithms for training feed forward networks, IEEE Trans. Neural Netw, 13, 5, pp. 1064-1074, (2002)

[2]

Baoquan K., Chunyan L., Shukang C., Flux-weakening-characteristic analysis of a new permanent-magnet synchronous motor used for electric vehicles, IEEE Trans. Plasma Science, 39, 1, pp. 511-516, (2011)

[3]

Gaur P., Singh B., Mittal A.P., Observer based position and speed estimation of IPM, Proc. of IEEE-International Conference, PEDES-2006, pp. 1-5, (2006)

[4]

Gaur P., Singh B., Mittal A.P., Artificial neural network controller and speed estimation of permanent magnet synchronous motor, Proc. of IEEE-International Conference, POWERCON- 2008, pp. 1-6, (2008)

[5]

Genduso F., Miceli R., Rando C., Galluzzo G.R., Back EMF sensorless-control algorithm for high-dynamic performance PMSM, IEEE Trans. Ind. Appl, 57, 6, pp. 2092-2100, (2010)

[6]

Huang G.B., Zhu Q.Y., Siew C.K., Extreme learning machine: Theory and applications, Neurocomputing, 70, pp. 489-501, (2005)

[7]

Huang G.B., Zhu Q.Y., Siew C.K., Extreme learning machine: Theory and applications, Neurocomputing, 70, pp. 489-501, (2006)

[8]

Inoue Y., Kawaguchi Y., Morimoto S., Sanada M., Performance improvement of sensorless ipmsm drives in a low-speed region using online parameter identification, IEEE Transactions on Industry Applications, 47, 2, pp. 798-804, (2011)

[9]

Lacher R.C., Hruska S.I., Kuncicky D.C., Back-propagation learning in expert networks, IEEE Transactions Neural Networks, 3, 1, pp. 62-72, (1992)

[10]

Lan Y., Soh Y.C., Huang G.B., Ensemble of online sequential extreme learning machine, Neurocomputing, 72, pp. 3391-3395, (2009)

← 1 2 →