Neural networks based nonlinear H∞ control for linear switched reluctance motor

H.Y. Li; Y.L. Liu; J. Wang; Y.K. Wong; Wai Lok Chan

Neural networks based nonlinear H∞ control for linear switched reluctance motor

H.Y. Li, Y.L. Liu, J. Wang, Y.K. Wong, Wai Lok Chan

The Hong Kong Polytechnic University

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

Abstract

This paper is concerned with Hinfin control problems for a class of uncertain nonlinear systems. In the procedure, neural networks (NNs) are used to model the nonlinear functions, Hinfin tracking controller is derived based on Lyapunov function and the notion of dissipativeness. The controller can not only guarantee the stability of the overall control system, but also attenuate the effect of both the external disturbance and NNs approximation error to a prescribed level. Furthermore, theoretical results are applied to a position tracking control of linear switched reluctance motor. Simulation studies are included to demonstrate the effectiveness of the method.

Original language	English
Title of host publication	ASCC 2009 : the proceedings of 2009 7th Asian Control Conference : Hong Kong Convention and Exhibition Centre, Hong Kong : August 27-29, 2009
Publisher	IEEE
Pages	796-801
Number of pages	6
ISBN (Electronic)	9788995605691
ISBN (Print)	9788995605622
Publication status	Published - 2009
Event	Asian Control Conference [ASCC] - Duration: 1 Jan 2009 → …

Conference

Conference	Asian Control Conference [ASCC]
Period	1/01/09 → …

Keywords

Hinfin control
Lyapunov methods
Linear motors
Machine control
Neurocontrollers
Nonlinear control systems
Position control
Reluctance motors
Stability
Uncertain systems

Cite this

@inproceedings{344ab21761e843edbc909d1d0858e2c9,

title = "Neural networks based nonlinear H∞ control for linear switched reluctance motor",

abstract = "This paper is concerned with Hinfin control problems for a class of uncertain nonlinear systems. In the procedure, neural networks (NNs) are used to model the nonlinear functions, Hinfin tracking controller is derived based on Lyapunov function and the notion of dissipativeness. The controller can not only guarantee the stability of the overall control system, but also attenuate the effect of both the external disturbance and NNs approximation error to a prescribed level. Furthermore, theoretical results are applied to a position tracking control of linear switched reluctance motor. Simulation studies are included to demonstrate the effectiveness of the method.",

keywords = "Hinfin control, Lyapunov methods, Linear motors, Machine control, Neurocontrollers, Nonlinear control systems, Position control, Reluctance motors, Stability, Uncertain systems",

author = "H.Y. Li and Y.L. Liu and J. Wang and Y.K. Wong and Chan, {Wai Lok}",

year = "2009",

language = "English",

isbn = "9788995605622",

pages = "796--801",

booktitle = "ASCC 2009 : the proceedings of 2009 7th Asian Control Conference : Hong Kong Convention and Exhibition Centre, Hong Kong : August 27-29, 2009",

publisher = "IEEE",

address = "United States",

note = "Asian Control Conference [ASCC] ; Conference date: 01-01-2009",

}

Neural networks based nonlinear H∞ control for linear switched reluctance motor. / Li, H.Y.; Liu, Y.L.; Wang, J. et al.
ASCC 2009 : the proceedings of 2009 7th Asian Control Conference : Hong Kong Convention and Exhibition Centre, Hong Kong : August 27-29, 2009. IEEE, 2009. p. 796-801.

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

TY - GEN

T1 - Neural networks based nonlinear H∞ control for linear switched reluctance motor

AU - Li, H.Y.

AU - Liu, Y.L.

AU - Wang, J.

AU - Wong, Y.K.

AU - Chan, Wai Lok

PY - 2009

Y1 - 2009

N2 - This paper is concerned with Hinfin control problems for a class of uncertain nonlinear systems. In the procedure, neural networks (NNs) are used to model the nonlinear functions, Hinfin tracking controller is derived based on Lyapunov function and the notion of dissipativeness. The controller can not only guarantee the stability of the overall control system, but also attenuate the effect of both the external disturbance and NNs approximation error to a prescribed level. Furthermore, theoretical results are applied to a position tracking control of linear switched reluctance motor. Simulation studies are included to demonstrate the effectiveness of the method.

AB - This paper is concerned with Hinfin control problems for a class of uncertain nonlinear systems. In the procedure, neural networks (NNs) are used to model the nonlinear functions, Hinfin tracking controller is derived based on Lyapunov function and the notion of dissipativeness. The controller can not only guarantee the stability of the overall control system, but also attenuate the effect of both the external disturbance and NNs approximation error to a prescribed level. Furthermore, theoretical results are applied to a position tracking control of linear switched reluctance motor. Simulation studies are included to demonstrate the effectiveness of the method.

KW - Hinfin control

KW - Lyapunov methods

KW - Linear motors

KW - Machine control

KW - Neurocontrollers

KW - Nonlinear control systems

KW - Position control

KW - Reluctance motors

KW - Stability

KW - Uncertain systems

M3 - Conference article published in proceeding or book

SN - 9788995605622

SP - 796

EP - 801

BT - ASCC 2009 : the proceedings of 2009 7th Asian Control Conference : Hong Kong Convention and Exhibition Centre, Hong Kong : August 27-29, 2009

PB - IEEE

T2 - Asian Control Conference [ASCC]

Y2 - 1 January 2009

ER -

Neural networks based nonlinear H∞ control for linear switched reluctance motor

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Keywords

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