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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振动与冲击 ›› 2016, Vol. 35 ›› Issue (21) : 196-202.

论文

孙宇新1，钱忠波1,2

作者信息 +

Independent RBFNN inverse decoupling control of the levitation subsystem of bearingless induction motor for NC machine

Yuxin SUN1, Zhongbo QIAN1,2

Author information +

文章历史 +

摘要

为了实现对数控机床用高速无轴承异步电动机(bearingless induction motor, BIM)动态解耦控制以实现降低悬浮电主轴抖动，本文提出了一种基于径向基函数神经网络(radial basis function neural network, RBFNN)的悬浮子系统自适应独立控制方法。首先，基于RBFNN构建了气隙磁链观测器，因为RBFNN具有较强的自学习和自适应能力，所以辨识的气隙磁链较为精确；其次，基于Hamilton-Jacobi-Isaacs (HJI)原理设计RBFNN逆系统鲁棒控制器，应用基于HJI不等式的RBFNN辨识系统模型不确定和外界干扰，提高系统的稳定性，悬浮子系统动态独立解耦控制得以实现；最后，将磁链辨识器和逆系统鲁棒控制器组成双RBFNN悬浮子系统逆独立控制系统。仿真和实验结果表明，采用该控制方法BIM系统能获得良好的动、静态性能。

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.

导出引用

孙宇新1，钱忠波1,2. 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[J]. 振动与冲击, 2016, 35(21): 196-202

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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