数控机床高速无轴承异步电动机悬浮子系统RBFNN逆独立解耦控制

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Abstract

To realize the dynamic decoupling of the high speed bearingless induction motor (BIM) for NC machine, a self-adaptive independent control method, based on radial basis function neural network (RBFNN), is proposed in this paper. Firstly, by this method, an air-gap flux observer is built to obtain the more accurate air-gap flux identifier for the strong self learning and adaptive ability of RBFNN. Furthermore, a RBFNN self-adaptive robust controller based on the Hamilton-Jacobi-Isaacs (HJI) is designed to realize decoupling control of the levitation subsystem stably and reliably. Finally, the self-adaptive independent control system with double RBFNN can be composed by the proposed air-gap flux observer and the self-adaptive robust controller. The simulation results have shown that the system has good dynamic and static performance. In addition, this proposed method not only realizes the decoupling control of the torque and radial suspension force, but also that of the radial suspension force in both two degrees of freedom of the system.

Key words： BIM decoupling control system RBFNN flux identification robust controller NC machine

Received: 15 October 2015 Published: 25 October 2016

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	Articles by authors
	Yuxin SUN1
	Zhongbo QIAN1
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Cite this article:

Yuxin SUN1,Zhongbo QIAN1,2. Independent RBFNN inverse decoupling control of the levitation subsystem of bearingless induction motor for NC machine[J]. JOURNAL OF VIBRATION AND SHOCK, 2016, 35(21): 196-202.

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http://jvs.sjtu.edu.cn/EN/ OR http://jvs.sjtu.edu.cn/EN/Y2016/V35/I21/196

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