Position control of a PM stepper motor using neural networks

Gang Feng

doi:10.1109/CDC.2000.912117

Position control of a PM stepper motor using neural networks

Gang Feng

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

9 Citations (Scopus)

Abstract

This paper considers position control of a PM stepper motor. A new control scheme is proposed based on a kind of exact linearization controller and a neural network based compensating controller. This scheme takes advantages of simplicity of the model based control approach and uses the neural network controller to compensate for the motor modeling uncertainties. The neural network is trained on line based on Lyapunov theory and thus its convergence is guaranteed.

Original language	English
Title of host publication	Proceedings of the 39th IEEE Conference on Decision and Control (Cat. No.00CH37187)
Publisher	IEEE
Pages	1766-1769
Volume	2
ISBN (Print)	0-7803-6638-7
DOIs	https://doi.org/10.1109/CDC.2000.912117
Publication status	Published - 2000
Event	39th IEEE Confernce on Decision and Control (2000 IEEE CDC) - Sydney Convention and Exhibition Centre, Sydney, Australia Duration: 12 Dec 2000 → 15 Dec 2000

Publication series

Name	IEEE Conference on Decision and Control
ISSN (Print)	0191-2216

Conference

Conference	39th IEEE Confernce on Decision and Control (2000 IEEE CDC)
Place	Australia
City	Sydney
Period	12/12/00 → 15/12/00

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10.1109/CDC.2000.912117

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title = "Position control of a PM stepper motor using neural networks",

abstract = "This paper considers position control of a PM stepper motor. A new control scheme is proposed based on a kind of exact linearization controller and a neural network based compensating controller. This scheme takes advantages of simplicity of the model based control approach and uses the neural network controller to compensate for the motor modeling uncertainties. The neural network is trained on line based on Lyapunov theory and thus its convergence is guaranteed.",

author = "Gang Feng",

year = "2000",

doi = "10.1109/CDC.2000.912117",

language = "English",

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booktitle = "Proceedings of the 39th IEEE Conference on Decision and Control (Cat. No.00CH37187)",

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note = "39th IEEE Confernce on Decision and Control (2000 IEEE CDC) ; Conference date: 12-12-2000 Through 15-12-2000",

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Feng, G 2000, Position control of a PM stepper motor using neural networks. in Proceedings of the 39th IEEE Conference on Decision and Control (Cat. No.00CH37187). vol. 2, IEEE Conference on Decision and Control, IEEE, pp. 1766-1769, 39th IEEE Confernce on Decision and Control (2000 IEEE CDC), Sydney, New South Wales, Australia, 12/12/00. https://doi.org/10.1109/CDC.2000.912117

Position control of a PM stepper motor using neural networks. / Feng, Gang.
Proceedings of the 39th IEEE Conference on Decision and Control (Cat. No.00CH37187). Vol. 2 IEEE, 2000. p. 1766-1769 (IEEE Conference on Decision and Control).

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

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PY - 2000

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N2 - This paper considers position control of a PM stepper motor. A new control scheme is proposed based on a kind of exact linearization controller and a neural network based compensating controller. This scheme takes advantages of simplicity of the model based control approach and uses the neural network controller to compensate for the motor modeling uncertainties. The neural network is trained on line based on Lyapunov theory and thus its convergence is guaranteed.

AB - This paper considers position control of a PM stepper motor. A new control scheme is proposed based on a kind of exact linearization controller and a neural network based compensating controller. This scheme takes advantages of simplicity of the model based control approach and uses the neural network controller to compensate for the motor modeling uncertainties. The neural network is trained on line based on Lyapunov theory and thus its convergence is guaranteed.

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DO - 10.1109/CDC.2000.912117

M3 - RGC 32 - Refereed conference paper (with host publication)

SN - 0-7803-6638-7

VL - 2

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ER -

Position control of a PM stepper motor using neural networks

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