Three degrees of freedom modeling and adaptive neural network control for long-stroke wafer stage

WANG Yi-guang; CHEN Xing-lin; LI Xiao-jie

doi:10.3788/OPE.20152301.0132

您当前的位置：

首页 >

文章列表页 >

Three degrees of freedom modeling and adaptive neural network control for long-stroke wafer stage

更新时间：2020-08-13

- Three degrees of freedom modeling and adaptive neural network control for long-stroke wafer stage
- Optics and Precision Engineering Vol. 23, Issue 1, Pages: 132-140(2015)
- 作者机构：
  
  1. 哈尔滨工业大学航天学院,黑龙江哈尔滨,150001
  2. 黑龙江省工业技术研究院,黑龙江哈尔滨,150001
- 作者简介：
- 基金信息：
- DOI：10.3788/OPE.20152301.0132
  CLC： TP273.2;TN305.7
- Received：04 May 2014，
  
  Revised：04 June 2014，
  
  Published：25 January 2015
- 稿件说明：
移动端阅览
王一光, 陈兴林, 李晓杰. 光刻机工件台宏动三自由度建模及自适应神经网络控制[J]. 光学精密工程, 2015,23(1): 132-140

WANG Yi-guang, CHEN Xing-lin, LI Xiao-jie. Three degrees of freedom modeling and adaptive neural network control for long-stroke wafer stage[J]. Editorial Office of Optics and Precision Engineering, 2015,23(1): 132-140
王一光, 陈兴林, 李晓杰. 光刻机工件台宏动三自由度建模及自适应神经网络控制[J]. 光学精密工程, 2015,23(1): 132-140 DOI： 10.3788/OPE.20152301.0132.

WANG Yi-guang, CHEN Xing-lin, LI Xiao-jie. Three degrees of freedom modeling and adaptive neural network control for long-stroke wafer stage[J]. Editorial Office of Optics and Precision Engineering, 2015,23(1): 132-140 DOI： 10.3788/OPE.20152301.0132.

摘要

提出了一种工件台宏动三自由度建模方法以解决光刻机工件台宏动部分在

和

方向的运动耦合问题并实现它的超精密长行程微米精度的跟踪定位.该建模方法将

方向电机的偏转角度作为被控对象并且在模型中包含了耦合效应对

方向运动的影响.基于此模型提出了一种自适应神经网络控制策略

该策略采用径向基函数(Radial Basis Function

RBF)神经网络对模型参数信息及外界非线性扰动进行实时在线估计

以减小未建模动态、电机齿槽力波动、端部效应、摩擦等扰动对控制系统性能的影响.通过对控制策略的理论推导和稳定性分析

保证了闭环控制系统的收敛性.最后在光刻机工件台上进行了

曲线跟踪定位试验

验证了宏动三自由度建模方法和控制策略的效果.试验结果显示:

和

方向的位置跟踪误差均小于3 μm

方向电机偏转角度小于1 μrad

满足工件台宏动部分跟踪定位精度的要求.

Abstract

A three degrees of freedom modeling method for a long-stroke wafer stage in lithography was proposed to solve the

X-Y

coupling problem of the long-stroke wafer stage and to achieve ultra-precision tracking with micron accuracy. In the modeling method

the rotation angle of

linear motor was considered as one of the controlled objects and the coupling effect on the moving in

direction was involved in the model. Then an adaptive neural network control method was presented based on the proposed model. The Radial Basis Function (RBF) neural network was used to estimate the model information and external nonlinear disturbances real-time online and to reduce the influences of unmodeled dynamics

cogging forces

end effect and friction on the control system. With the theoretical derivation and stability analysis

the convergence of the closed-loop system was guaranteed. Finally

the effectiveness of the modeling and the control method were verified by a

-curve tracking experiment on the actual long-stroke wafer stage of the lithography. The experiment results show that the tracking errors of the

and

linear motors are less than 3 μm and the rotation angle of

motor is less than 1 μrad. The tracking errors meet the design requirements.

关键词

Keywords

references

TAN K K, LEE T H, HUANG S. Precision Motion Control: Design and Implementation [M]. Springer, 2008: 20-24.

ALBENDER F, LAMPAERT V, SWEVERS J. The generalized Maxwell-slip model: a novel model for friction simulation and compensation [J]. Automatic Control, IEEE Transactions on, 2005, 50(11): 1883-1887.

LIAO T L, CHIEN T I. An exponentially stable adaptive friction compensator [J]. Automatic Control, IEEE Transactions on, 2000, 45(5): 977-980.

TAN K K, HUANG S N, LEE T H. Robust adaptive numerical compensation for friction and force ripple in permanent-magnet linear motors [J]. Magnetics, IEEE Transactions on, 2002, 38(1): 221-228.

AHN H S, CHEN Y Q, DOU H. State-periodic adaptive compensation of cogging and coulomb friction in permanent magnet linear motors[C].American Control Conference, Proceedings of the 2005, IEEE, 2005: 3036-3041.

WANG C C, TOMIZUKA M. Design of robustly stable disturbance observers based on closed loop consideration using H∞ optimization and its applications to motion control systems[C].American Control Conference, Proceedings of the 2004, IEEE, 2004, 4: 3764-3769.

SU W T, LIAWC M. Adaptive positioningcontrol for a LPMSM drive based on adapted inverse model and robust disturbance observer [J]. Power Electronics, IEEE Transactions on, 2006, 21(2): 505-517.

XU L, YAO B. Output feedback adaptive robust precision motion control of linear motors [J].Automatica, 2001, 37(7): 1029-1039.

YAO B, XU L. Adaptive robust motion control of linear motors for precision manufacturing [J]. Mechatronics, 2002, 12(4): 595-616.

PASZKE W, MERRY R, MOLENGRAFT R. Iterative learning control by two-dimensional system theoryapplied to a motion system[C].American Control Conference, IEEE, 2007: 5484-5489.

AHN H S, CHEN Y Q, MOORE K L. Iterative learning control: brief survey and categorization [J]. IEEE Transactions on Systems Man and Cybernetics Part C, Applications and Reviews, 2007, 37(6): 1099.

李朋志, 闫丰, 葛川, 等. 压电驱动器的开闭环迭代学习控制 [J]. 光学精密工程, 2014, 22(2). LI P ZH, YAN F, GE CH, et al.. Open-closed loop iterative learning control of piezoelectric actuators [J]. Opt. Precision Eng., 2014, 22(2). (in Chinese)

李朋志, 葛川, 苏志德, 等. 基于动态模糊系统模型的压电陶瓷驱动器控制 [J]. 光学精密工程, 2013, 21(2). LI P ZH, GE CH, SU ZH D, et al.. Control of piezoelectric ceramic actuator via dynamic fuzzy system model [J]. Opt. Precision Eng., 2013, 21(2): 394-399. (in Chinese)

CHEUNG J W F, HUNG Y S. Robust learning control of a high precision planar parallel manipulator [J]. Mechatronics, 2009, 19(1): 42-55.

王一光,陈兴林,李晓杰. 基于PMLM的PDμ运动控制器的设计研究 [J]. 国防科技大学学报,2014,36(1):142-147. WANG Y G, CHEN X L, LI X J. A PDμ motion controller design method for PMLM [J]. Journal of National University of Defense Technology, 2014, 36(1): 142-147. (in Chinese)

丁策, 戴明, 李嘉全, 等. 光电陀螺稳定平台的分数阶控制 [J]. 光学精密工程, 2014, 22(2):383-389. DING C, DAI M, LI J Q, et al.. Fractional control of stabilized platform for photoelectric gyro [J]. Opt. Precision Eng., 2014, 22(2):383-389. (in Chinese)

LUO Y, CHEN Y Q, AHN H S, et al.. Fractional order robust control for cogging effect compensation in PMSM position servo systems: stability analysis and experiments [J]. Control Engineering Practice, 2010, 18(9): 1022-1036.

BARBOSA R S, TENREIRO MACHADO J A, JESUS I S. Effect of fractional orders in the velocity control of a servo system [J]. Computers & Mathematics with Applications, 2010, 59(5): 1679-1686.

LI Y, QIANG S, ZHUANG X. Robust and adaptive backstepping control for nonlinear systems using RBF neural networks [J]. Neural Networks, IEEE Transactions on, 2004, 15(3): 693-701.

HUANG S, TAN K K, LEE T H, et al.. Adaptive control of mechanical systems using neural networks [J]. Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on, 2007, 37(5): 897-903.

WANG S, YU D L. Adaptive RBF network for parameter estimation and stable air-fuel ratio control [J]. Neural Networks, 2008, 21(1): 102-112.

ZHU Q, FEI S, ZHANG T, et al.. Adaptive RBF neural-networks control for a class of time-delay nonlinear systems [J]. Neurocomputing, 2008, 71(16): 3617-3624.

WANG S, YU D L. Adaptive RBF network for parameter estimation and stable air-fuel ratio control [J]. Neural Networks, 2008, 21(1): 102-112.

SHUZHI S G,LEE T H, HARRIS C J. Adaptive Neural Network Control of Robotic Manipulators [M]. World Scientific, 1998.

ZHANG T, GE S S. Adaptive neural network tracking control of MIMO nonlinear systems with unknown dead zones and control directions [J]. Neural Networks, IEEE Transactions on, 2009, 20(3): 483-497.

LIAW H C, SHIRINZADEH B, SMITH J. Robust neural network motion tracking control of piezoelectric actuation systems for micro/nanomanipulation [J]. Neural Networks, IEEE Transactions on, 2009, 20(2): 356-367.

LIAW H C, SHIRINZADEH B. Neural network motion tracking control of piezo-actuated flexure-based mechanisms for micro-/nanomanipulation [J]. Mechatronics, IEEE/ASME Transactions on, 2009, 14(5): 517-527.

WANG S, YU D L. Adaptive RBF network for parameter estimation and stable air-fuel ratio control [J]. Neural Networks, 2008, 21(1): 102-112.

ZHU Q, FEI S, ZHANG T, et al.. Adaptive RBF neural-networks control for a class of time-delay nonlinear systems [J]. Neurocomputing, 2008, 71(16): 3617-3624.

KOBAYASHI H, OZAWA R. Adaptive neural network control of tendon-driven mechanisms with elastic tendons [J]. Automatica, 2003, 39(9): 1509-1519.

CHENG X P, PATEL R V. Neural network based tracking control of a flexible macro-micro manipulator system [J]. Neural Networks, 2003,16(2): 271-286.

LEWIS F L, YESILDIREK A, LIU K. Multilayer neural-net robot controller with guaranteed tracking performance [J]. Neural Networks, IEEE Transactions on, 1996, 7(2): 388-399.

Views

524

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

No data

Related Author

No data

Related Institution

No data

AI问答

Address：No.3888 Dong Nanhu Road, Changchun, Jilin, China Postal code：130033
Tel：0431-86176855 Email：gxjmgc@ciomp.ac.cn
Technical support is provided by Beijing Founder electronics co., LTD 吉ICP备11002662号-17 京公网安备11010802024621
It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.

⁰