Application of type-II fuzzy neural network to adaptive double axis motion control system

CHEN Xiang-jian; LI Di; BAI Yue; XU Zhi-jun

doi:10.3788/OPE.20111907.1643

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Application of type-II fuzzy neural network to adaptive double axis motion control system

Article | 更新时间：2020-08-12

- Application of type-II fuzzy neural network to adaptive double axis motion control system
- Optics and Precision Engineering Vol. 19, Issue 7, Pages: 1643-1650(2011)
- 作者机构：
  
  中国科学院长春光学精密机械及物理研究所,吉林长春,130033
- 作者简介：
- 基金信息：
- DOI：10.3788/OPE.20111907.1643
  CLC： TP273.2;TP183
- Received：13 December 2010，
  
  Revised：15 February 2011，
  
  Published Online：25 July 2011，
  
  Published：25 July 2011
- 稿件说明：
移动端阅览
陈向坚, 李迪, 白越, 续志军. 模糊神经网络在自适应双轴运动控制系统中的应用[J]. 光学精密工程, 2011,19(7): 1643-1650

CHEN Xiang-jian, LI Di, BAI Yue, XU Zhi-jun. Application of type-II fuzzy neural network to adaptive double axis motion control system[J]. Editorial Office of Optics and Precision Engineering, 2011,19(7): 1643-1650
陈向坚, 李迪, 白越, 续志军. 模糊神经网络在自适应双轴运动控制系统中的应用[J]. 光学精密工程, 2011,19(7): 1643-1650 DOI： 10.3788/OPE.20111907.1643.

CHEN Xiang-jian, LI Di, BAI Yue, XU Zhi-jun. Application of type-II fuzzy neural network to adaptive double axis motion control system[J]. Editorial Office of Optics and Precision Engineering, 2011,19(7): 1643-1650 DOI： 10.3788/OPE.20111907.1643.

摘要

为了更好地提高工艺加工平台的精确度

结合自适应控制与区间二型模糊神经网络理论设计了一个双轴运动控制系统。该系统通过控制两个场向永磁同步电机来定位

X-Y

双轴运动转子的位置

从而跟踪预设的蝶形曲线。针对由区间二型模糊神经网络的有限规则产生的不可避免的逼近误差和优化的参数向量等集中不确定性因素

设计了自适应集中不确定性估计律

并通过在线鲁棒补偿器处理集中不确定性的值。最后

通过TMS320C32数字信号处理器运行了本文提出的控制算法。实验结果验证了基于区间二型模糊神经网络设计的双轴运动控制系统的轨迹跟踪精确度较高。与一型模糊神经网络控制系统相比

区间二型模糊神经网络控制系统具有更好的控制性能

鲁棒性更强。

Abstract

An adaptive double axis motion control system to improve the accuracy of processing platforms was designed by combining the adaptive technology and the interval type-II fuzzy neural network theory.The system controled two field oriented permanent magnet synchronous motors to locate the

X-Y

double axis motion rotor to track the butterfly contour. Meanwhile

a robust compensator was proposed to confront the Lumped uncertainty

including the inevitable approximation error due to the finite rules of the interval type-II fuzzy neural network

optimal parameter vectors and so on. Finally

the proposed control algorithm was implemented in a TMS320C32 digital signal processor. The experimental results indicate that the butterfly contour tracking performance of the double axis motion control system is improved significantly

and the control system based on the interval type-II fuzzy neural network is more robust than that based on the type-I fuzzy neural network for different uncertainties.

关键词

Keywords

references

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