基于神经网络逆系统理论无轴承异步电动机解耦控制

Abstract
Figure/Table
References
Related Citation (15)

Download: PDF (550 KB) HTML (1 KB)
Export: BibTeX | EndNote (RIS)

Abstract A bearingless induction motor is a multi-variable, nonlinear and strong-coupled system. The cross-coupled relation exists among speed and radial forces. In order to realize the rotor suspending and the motor working steadily, it is necessary to realize dynamic decoupling control among speed and radial forces. In this paper, a novel decoupling control method based on neural networks inverse system is proposed for bearingless induction motors. Theoretical analysis shows that by using the proposed method the system is decoupled into two independent linear displacement subsystems, an independent linear rotor speed subsystem and an independent linear magnetic linkage subsystem. The design of outer controllers becomes easier, and the whole system control performance is further improved. At last, taking bearingless induction motor system which adapts the proposed method as object, the study of simulation and primary experiment are done. The validity of the proposed method is proved by the simulation and primary experiment results.

Key words： Bearingless induction motor complex-controlled object neural networks inverse system decoupling control

Received: 26 March 2008 Published: 04 March 2014

PACS:

TM346

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	Sun Xiaodong
	Zhu Huangqiu

Cite this article:

Sun Xiaodong,Zhu Huangqiu. Decoupling Control of Bearingless Induction Motors Based on Neural Network Inverse System Method[J]. Transactions of China Electrotechnical Society, 2010, 25(1): 43-49.

URL:

http://dgjsxb.ces-transaction.com/EN/ OR http://dgjsxb.ces-transaction.com/EN/Y2010/V25/I1/43

[1] Hiromi T, Katou T, Chiba A, et al. A novel magnetic suspension-force compensation in bearingless induction-motor drive with squirrel-cage rotor[J]. IEEE Transactions on Industry Applications, 2007, 43(1): 66-76.
[2] Schoeb R, Bichsel J. Vector control of the bearingless motor[C]. The fourth International Symposium on Magnetic Bearings, Zürich, Switzerland, 1994: 327-332.
[3] 朱熀秋, 沈玉祥, 张腾超, 等. 无轴承异步电机数学模型与解耦控制[J]. 电机与控制学报, 2007, 11(4)：321-325.
[4] Suzuki T, Chiba A, Rahman A, et al. An air-gap-flux-oriented vector controller for stable operation of bearingless induction motors[J]. IEEE Transactions on Industry Applications, 2000, 36(4): 1069-1076.
[5] Zhu Huangqiu, Zhou Yang, Li Tianbo, et a1. Decoupling control of 5 degrees of freedom bearingless induction motors using α-th order inverse system method[J]. Acta Automatica Sinica, 2007, 33(3): 273-278.
[6] 戴先中. 多变量非线性系统的神经网络逆控制方法[M]. 北京：科学出版社, 2006.
[7] 贺益康, 年珩, 阮秉涛. 感应型无轴承电机的优化气隙磁场定向控制[J]. 中国电机工程学报, 2004, 24(6)：116-121.
[8] Fujishiro T, Hanawa R, Sakata Y, et al. An analysis of an induction bearingless motor with a squirrel cage rotor[C]. The Eighth International Symposium on Magnetic Bearing, Mito, Japan, 2002: 253-258.
[9] Chiba A, Power T D, Rzhman M A. Analysis of noload characteristics of a bearingless induction motor[J]. IEEE Transactions on Industry Applications, 1995, 31(1): 77-83.
[10] Chiba A, Power D T, Rahman M A. Characteristics of a bearingless induction motor[J]. IEEE Transactions on Magnetic, 1991, 27(6): 5199-5201.
[11] Chiba A, Fukao T, Ichikawa O, et al. Magnetic bearings and bearingless drives[M]. Boston, MA: Elsevier Newnes Press, 2005.
[12] 刘国海, 孙玉坤, 张浩, 等. 基于神经网络逆系统的磁悬浮开关磁阻电动机的解耦控制[J]. 电工技术学报, 2005, 20(9)：39-43.