基于永磁直线同步电机的光刻机掩模台鲁棒自适应神经网络控制

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Abstract This paper presents a robust adaptive neural network tracking compensation control strategy based on permanent magnetic linear synchronous motor (PMLSM) for long-stroke reticle stage of lithography. It can estimate the model uncertainty and external nonlinear disturbance real-time online by radial basis function (RBF) neural network. A long-stroke PMLSM model of reticle stage based on parametric uncertainty and external disturbance was established. The derivation of the control strategy and the theoretical stability were analyzed. It was shown that the proposed model can guarantee convergence of the position error and velocity error. The actual effectiveness of this control strategy was verified by a fifth-order S-curve tracking experiment on the long-stroke reticle stage of lithography. The experimental data showed that the tracking accuracy met the design requirements well. This strategy doesn’t require precise modeling of the actual system parameters and the external disturbances which are difficult to measure. It is very suitable for the application in precision motion control field.

Key words： Permanent magnetic linear synchronous motor reticle stage of lithography robust adaptive neural network motion control

Received: 08 April 2014 Published: 01 April 2016

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TM341

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	Wang Yiguang
	Li Xiaojie
	Chen Xinglin

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Wang Yiguang,Li Xiaojie,Chen Xinglin. A Robust Adaptive Neural Network Control Method Based on Permanent Magnetic Linear Synchronous Motor for the Reticle Stage of Lithography[J]. Transactions of China Electrotechnical Society, 2016, 31(6): 38-46.

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https://dgjsxb.ces-transaction.com/EN/ OR https://dgjsxb.ces-transaction.com/EN/Y2016/V31/I6/38

[1] Heertjes M F, Nijmeijer H. Self-tuning of a switching controller for scanning motion systems[J]. Mecha- tronics, 2012, 22(3): 310-319.
[2] Mishra S, Coaplen J, Tomizuka M. Precision posi- tioning of wafer scanners segmented iterative learning control for nonrepetitive disturbances (applications of control)[J]. IEEE Control Systems, 2007, 27(4): 20- 25.
[3] Tan K K, Lee T H, Huang S. Precision motion control: design and implementation[M]. Springer, 2008: 20-24.
[4] Heertjes M, Hennekens D, Steinbuch M. MIMO feed-forward design in wafer scanners using a gradient approximation-based algorithm[J]. Control Engineering Practice, 2010, 18(5): 495-506.
[5] 石阳春, 周云飞, 李鸿, 等. 长行程直线电机的迭代学习控制[J]. 中国电机工程学报, 2007, 27(24): 92-96.
Shi Yangchun, Zhou Yunfei, Li Hong, et al. Research on iterative learning control to long stroke linear motor[J]. Proceedings of the CSEE, 2007, 27(24): 92-96.
[6] Armstrong-Hélouvry B, Dupont P, De Wit C C. A survey of models, analysis tools and compensation methods for the control of machines with friction[J]. Automatica, 1994, 30(7): 1083-1138.
[7] Lee S W, Kim J H. Robust adaptive stick-slip friction compensation[J]. IEEE Transactions on Industrial Elec- tronics, 1995, 42(5): 474-479.
[8] Al-Bender F, Lampaert V, Swevers J. The genera- lized Maxwell-slip model: a novel model for friction simulation and compensation[J]. IEEE Transactions on Automatic Control, 2005, 50(11): 1883-1887.
[9] Swevers J, Al-Bender F, Ganseman C G, et al. An integrated friction model structure with improved presliding behavior for accurate friction compens- ation[J]. IEEE Transactions on Automatic Control, 2000, 45(4): 675-686.
[10] Liao T L, Chien T I. An exponentially stable adaptive friction compensator[J]. IEEE Transactions on Auto- matic Control, 2000, 45(5): 977-980.
[11] Tan K K, Huang S N, Lee T H. Robust adaptive numerical compensation for friction and force ripple in permanent-magnet linear motors[J]. IEEE Transa- ctions on Magnetics, 2002, 38(1): 221-228.
[12] Xu L, Yao B. Output feedback adaptive robust precision motion control of linear motors[J]. Auto- matica, 2001, 37(7): 1029-1039.
[13] Yao B, Xu L. Adaptive robust motion control of linear motors for precision manufacturing[J]. Mecha- tronics, 2002, 12(4): 595-616.
[14] 赖志林, 刘向东, 陈振, 等. 压电陶瓷执行器的滑模迭代控制[J]. 电工技术学报, 2013, 28(增1): 389-396.
Lai Zhilin, Liu Xiangdong, Chen Zhen, et al. Sliding mode iterative learning control of the piezoceramic actuators[J]. Transactions of China Electrotechnical Sosiety, 2013, 28(S1): 389-396.
[15] 潘再平, 罗星宝. 基于迭代学习控制的开关磁阻电机转矩脉动抑制[J]. 电工技术学报, 2010, 25(7): 51-55.
Pan Zaiping, Luo Xingbao. Torque ripple minimi- zation of switched reluctance motor based on iterative learning control[J]. Transactions of China Electrotechnical Society, 2010, 25(7): 51-55.
[16] 杨俊友, 刘永恒, 白殿春, 等. 基于迭代学习与小波滤波器的永磁直线伺服系统扰动抑制[J]. 电工技术学报, 2013, 28(3): 87-92.
Yang Junyou, Liu Yongheng, Bai Dianchun, et al. Disturbance rejection for PMLSM based on iterative learning control and wavelet filter[J]. Transactions of China Electrotechnical Society, 2013, 28(3): 87-92.
[17] Kaiji S, Shimokohbe A. Robustness evaluation of new practical control for PTP positioning systems[C]// IEEE/ASME International Conference on Advanced Intelligent Mechatronics Proceedings, 2001, 2: 843-848.
[18] Sato K, Shimokohbe A. Robustness evaluation of new practical control for PTP positioning systems[C]// IEEE/ASME International Conference on Advanced Intelligent Mechatronics, 2001, 2: 843-848.
[19] Sato K, Shimokohbe A. Characteristics of practical control for point-to-point (PTP) positioning systems: Effect of design parameters and actuator saturation on positioning performance[J]. Precision Engineering, 2003, 27(2): 157-169.
[20] Albagul A. Performance improvement of practical control method for positioning systems in the presence of actuator saturation[C]//IEEE Proceedings of the International Conference on Control Appli- cations, 2004, 1: 296-302.
[21] Maeda G J, Sato K. Practical control method for ultra-precision positioning using a ballscrew mecha- nism[J]. Precision Engineering, 2008, 32(4): 309-318.
[22] Sato K, Maeda G J. A practical control method for precision motion—improvement of NCTF control method for continuous motion control[J]. Precision Engineering, 2009, 33(2): 175-186.
[23] Antsaklis P J. Neural networks for control systems[J]. IEEE Transactions on Neural Networks, 1990, 1(2): 242-244.
[24] Barron A R. Approximation and estimation bounds for artificial neural networks[J]. Machine Learning, 1994, 14(1): 115-133.
[25] Barron A R. Universal approximation bounds for superpositions of a sigmoidal function[J]. IEEE Transa- ctions on Information Theory, 1993, 39(3): 930-945.
[26] Chen T, Chen H. Approximation capability to functions of several variables, nonlinear functionals, and operators by radial basis function neural net- works[J]. IEEE Transactions on Neural Networks, 1995, 6(4): 904-910.
[27] 汪先兵, 费树岷, 徐清扬, 等. BP神经网络PID控制磁真空开关储能电容恒流充电特性分析[J]. 电工技术学报, 2015, 30(10): 212-218.
Wang Xianbing, Fei Shumin, Xu Qingyang, et al. Constant current charging characteristic analysis of storage capacitor based on BP neural network PID control for permanent magnet vacuum switch[J]. Transactions of China Electrotechnical Sosiety, 2015, 30(10): 212-218.
[28] 罗新, 牛海清, 来立永, 等. 粒子群优化自适应小波神经网络在带电局放信号识别中的应用[J]. 电工技术学报, 2014, 29(10): 326-333.
Luo Xin, Niu Haiqing, Lai Liyong, et al. Application of adaptive wavelet neural network based on particle swarm optimization algorithm in online PD pattern recognition[J]. Transactions of China Electrotech- nical Sosiety, 2014, 29(10): 326-333.
[29] Lin F J, Chou P H, Kung Y S. Robust fuzzy neural network controller with nonlinear disturbance observer for two-axis motion control system[J]. IET Control Theory & Applications, 2008, 2(2): 151-167.
[30] Kim H M, Park S H, Han S I. Precise friction control for the nonlinear friction system using the friction state observer and sliding mode control with recurrent fuzzy neural networks[J]. Mechatronics, 2009, 19(6): 805-815.
[31] Han S I, Lee K S. Robust friction state observer and recurrent fuzzy neural network design for dynamic friction compensation with backstepping control[J]. Mechatronics, 2010, 20(3): 384-401.
[32] Baruch I S, Beltran Lopez R, Olivares Guzman J L, et al. A fuzzy-neural multi-model for nonlinear systems identification and control[J]. Fuzzy Sets and Systems, 2008, 159(20): 2650-2667.
[33] Lin F J, Shieh P H, Shen P H. Robust recurrent- neural-network sliding-mode control for the X-Y table of a CNC machine[J]. IEE Proceedings—Control Theory and Applications, 2006, 153(1): 111-123.
[34] Shuzhi S G, Lee T H, Harris C J. Adaptive neural network control of robotic manipulators[M]. Singapore: World Scientific, 1998.
[35] Lewis F L. Neural network control of robot mani- pulators and nonlinear systems[M]. Bristol: CRC Press, 1999.
[36] Talebi H A, Patel R V, Khorasani K. Control of flexible-link manipulators using neural networks[M]. London: Springer, 2001.
[37] Liaw H C, Shirinzadeh B, Smith J. Robust neural network motion tracking control of piezoelectric actuation systems for micro/nanomanipulation[J]. IEEE Transactions on Neural Networks, 2009, 20(2): 356-367.
[38] 陈幼平, 杜志强, 艾武, 等. 一种短行程直线电机的数学模型及其实验研究[J]. 中国电机工程学报, 2005, 25(7): 131-136.
Chen Youping, Du Zhiqiang, Ai Wu, et al. Research on model of a new short-stroke linear motor and its experiments[J]. Proceedings of the CSEE, 2005, 25(7): 131-136.
[39] Tan K K, Huang S N, Dou H F, et al. Adaptive robust motion control for precise trajectory tracking appli- cations[J]. ISA Transactions, 2001, 40(1): 57-71.
[40] Wang L X. A course in fuzzy systems and control[M]. Upper Saddle River: Prentice Hall, 1997.
[41] Park J, Sandberg I W. Universal approximation using radial-basis-function networks[J]. Neural Comput- ation, 1991, 3(2): 246-257.
[42] Liaw H C, Shirinzadeh B. Neural network motion tracking control of piezo-actuated flexure-based mech- anisms for micro-/nanomanipulation[J]. IEEE/ ASME Transactions on Mechatronics, 2009, 14(5): 517-527.