Neural network control for piezo-actuator using sliding-mode technique

WEI Qiang; ZHANG Cheng-jin; ZHANG Dong; WANG Chun-ling

doi:10.3788/OPE.20122005.1055

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Neural network control for piezo-actuator using sliding-mode technique

Article | 更新时间：2020-08-12

- Neural network control for piezo-actuator using sliding-mode technique
- Optics and Precision Engineering Vol. 20, Issue 5, Pages: 1055-1063(2012)
- 作者机构：
  
  1. 泰山学院物理与电子工程学院,山东泰安,271021
  2. 青岛理工大学自动化工程学院,山东青岛 266033
  3. 山东大学电气工程学院,山东济南,250061
  4. 山东大学控制科学与工程学院,山东济南 250061
- 作者简介：
- 基金信息：
- DOI：10.3788/OPE.20122005.1055
  CLC： TN384;TP183
- Received：19 December 2011，
  
  Revised：15 February 2012，
  
  Published Online：10 May 2012，
  
  Published：10 May 2012
- 稿件说明：
移动端阅览
魏强, 张承进, 张栋, 王春玲. 压电陶瓷驱动器的滑模神经网络控制[J]. 光学精密工程, 2012,20(5): 1055-1063

WEI Qiang, ZHANG Cheng-jin, ZHANG Dong, WANG Chun-ling. Neural network control for piezo-actuator using sliding-mode technique[J]. Editorial Office of Optics and Precision Engineering, 2012,20(5): 1055-1063
魏强, 张承进, 张栋, 王春玲. 压电陶瓷驱动器的滑模神经网络控制[J]. 光学精密工程, 2012,20(5): 1055-1063 DOI： 10.3788/OPE.20122005.1055.

WEI Qiang, ZHANG Cheng-jin, ZHANG Dong, WANG Chun-ling. Neural network control for piezo-actuator using sliding-mode technique[J]. Editorial Office of Optics and Precision Engineering, 2012,20(5): 1055-1063 DOI： 10.3788/OPE.20122005.1055.

摘要

由于压电陶瓷驱动器的迟滞非线性严重影响其定位精度

本文提出了一种滑模神经网络控制方法来改善它的性能。用径向基函数神经网络的输出作滑模控制的等价控制量

由迟滞补偿器估计控制器参数误差、外部扰动和近似计算所造成的不确定量对神经网络的输出控制量进行补偿

从而使驱动器系统状态保持在滑模平面上。基于Lyapunov稳定性理论推导了控制器和补偿器的自适应调节律

分析了控制系统的收敛性和稳定性。以可变幅值的低频三角波为参考位移量对控制系统进行了实验测试与分析

结果表明

只采用神经网络控制时的平均定位误差为0.43 m

最大误差为0.77 m

而采用滑模控制方法对神经网络控制量进行补偿后

平均定位误差减小为0.27 m

最大误差减小为0.49 m

定位精度有了显著的提高。

Abstract

As the positioning precision of piezo-actuators is always severely deteriorated by hysteresis nonlinear effect

this paper proposes a neural network control scheme with a hysteresis compensator based on sliding-mode technique to improve the performance of the piezo-actuators. A Radial Basic Function Neural Network (RBFNN) was developed as a equivalent control value in the sliding-mode control and the hysteresis compensator was used to estimate the lumped uncertainty caused by the varying parameters in the RBFNN

external disturbance and the approximate algorithm to compensate the output signal of the RBFNN. For the above steps

the dynamics of actuator was guaranteed on the sliding surface. The adaptive tuning laws of the network and the compensator were derived on the basis of Lyapunov stability theory

and the convergence and stability of the control system were proved theoretically. A low frequency triangle reference displacement with a variable amplitude was used to detect and analyze the effect of the proposed control method. Experimental results show that the mean and maximal positioning errors by the tradition neural network are 0.43 m and 0.77 m respectively

but these errors can be reduced to 0.27 m and 0.49 m under the sliding model controller. Finally

the positioning precision is approved evidently.

关键词

Keywords

references

董维杰, 宋志杨, 崔岩. 压电陶瓷管的微位移测量与非线性校正 [J]. 光学精密工程, 2009, 17(9): 2212-2217. DONG W J, SONG ZH Y, CUI Y. Measurement and nonlinear correction for micro-displacement of piezoceramic tube[J]. Opt. Precision Eng., 2009, 17(9): 2212-2217. (in Chinese)[2] LIU Y T, CHANG K M, LI W Z. Model reference adaptive control for a piezo-positioning system[J]. Precision Engineering, 2010, 34(1): 62-69.[3] 张栋, 张承进, 魏强. 压电微动工作台的动态迟滞模型 [J]. 光学精密工程, 2009, 17(3): 549-556. ZHANG D, ZHANG CH J, WEI Q. Dynamic hysteresis model of piezopositioning stage[J]. Opt. Precision Eng., 2009, 17(3): 549-556. (in Chinese)[4] WEN Y K. Method of random vibration of hysteresis systems[J]. Journal of the Engineering Mechanics Division, 1976, 102(2): 249-263.[5] GE P, JOUANEH M. Generalized Preisach model for hysteresis nonlinearity of piezoceramic actuators[J]. Precision Engineering, 1997, 20 (2):99-111.[6] JINHYOUNG O, BERNSTEIN D S. Semilinear Duhem model for rate-independent and rate-dependent hysteresis[J]. IEEE Transactions on Automatic Control, 2005, 50(5): 631-645.[7] SHEN J C, JYWEA W Y, CHIANG H K, et al.. Precision tracking control of a piezoelectric-actuated system[J]. Precision Engineering, 2008, 32 (2): 71-78.[8] 龚大成, 唐志峰, 吕福在, 等. 非线性Preisach理论与超磁致伸缩执行器高阶迟滞建模 [J].机械工程学报, 2009, 45(12): 252-256. GONG D CH, TANG ZH F, LV F Z, et al.. Nonlinear Preisach model and high order hysteresis modeling for giant magnetostrictive actuator[J]. Journal of Mechanical Engineering, 2009, 45(12): 252-256. (in Chinese)[9] TZEN J J, JENG S L, CHIENG W H. Modeling of piezoelectric actuator for compensation and controller design[J]. Precision Engineering, 2003, 27(1): 70-86.[10] SHIEH H J, LIN F J, HUANG P K, et al.. Adaptive tracking control solely using displacement feedback for a piezo-positioning mechanism . IEE Proc. Control Theory Appl., 2004, 151 (5): 653-660.[11] RU CH H, SUN L N. Improving positioning accuracy of piezoelectric actuators by feedforward hysteresis compensation based on a new mathematical model[J]. Review of Scientific Instruments, 2005, 76(9): 095111-1-095111-8.[12] CHEN C S. Dynamic structure neural-fuzzy networks for robust adaptive control of robot manipulators[J]. IEEE Transactions on Industrial Electronics, 2008, 55(9): 3402-3414.[13] GALIAS Z, YU X H. Analysis of zero-order holder discretization of two-dimensional sliding-mode control systems[J]. IEEE Transactions on Circuits And Systems-II: Express Briefs, 2008, 55(12): 1269-1273.[14] 刘春芳, 安明伟, 王丽梅, 等. 数控机床进给用磁悬浮系统的积分滑模控制 [J]. 组合机床与自动化加工技术, 2009(11):46-49. LIU CH F, AN M W, WANG L M, et al.. CNC machine tool feed system using magnetic levitation integral sliding mode control[J]. Modular Machine Tool & Automatic Manufacturing Technique, 2009(11): 46-49. (in Chinese)[15] 孙宝玉. 柔性压电式微位移机构动态特性的实验研究 [J]. 微细加工技术, 2008(2): 33-36. SUN B Y. Experimental research on dynamic characteristic of flexible micro-displacement mechanism based on piezoelectric actuator[J]. Microfabrication Technology, 2008(2): 33-36. (in Chinese)[16] LIN F J, SHIEH H J, HUANG P K, et al.. Adaptive control with hysteresis estimation and compensation using RFNN for piezo-actuator[J]. IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 2006, 53(9): 1649-1661.[17] HUANG S J, CHIU C M. Optimal LuGre friction model identification based on genetic algorithm and sliding mode control of a piezoelectric-actuating table[J]. IEEE Transactions of the Institute of Measurement & Control, 2009, 31(2): 181-203.[18] 汪文英, 沈斌, 陆忠华, 等. 基于均匀设计与Powell算法的全局最优化算法及并行实现 [J]. 计算机应用研究, 2007, 24(5): 169-172. WANG W Y, SHEN B, LU ZH H, et al.. Global optimization algorithm based on uniform design and Powell method and parallel implementation[J]. Application Research of Computers, 2007, 24(5): 169-172. (in Chinese)[19] CANUDAS W C, OLSSON H, ASTROM K J, et al.. A new model for control of systems with friction[J]. IEEE Transactions on Automatic Control, 1995, 40(3): 419-425.[20] 赖志林, 刘向东, 耿洁, 等. 压电陶瓷执行器迟滞的滑模逆补偿控制 [J]. 光学精密工程, 2011, 19(6): 1281-1290. LAI ZH L, LIU X D, GENG J, et al.. Sliding mode control of hysteresis of piezoelectric actuator based on inverse Preisach compensation[J]. Opt. Precision Eng., 2011, 19(6): 1281-1290. (in Chinese)[21] SURESH S, KANNAN N, SUNDARARAJAN N, et al.. Neural adaptive control for vibration suppression in composite fin-tip of aircraft[J]. International Journal of Neural Systems, 2008, 18(3): 219-231.[22] TOMBUL G S, BANKS S P, AKTURK N, et al.. Sliding mode control for a class of non-affine nonlinear systems[J]. Nonlinear Analysis, 2009, 71(12): 1589-1597.[23] CASTILLO T B, DI G S, LOUKIANOV A G, et al.. Discrete time sliding mode control with application to induction motors[J].Automatica, 2008, 44(12): 3036-3045.[24] LIN F J, SHIEH H J, HUANG P K. Adaptive wavelet neural network control with hysteresis estimation for piezo-positioning mechanism[J]. IEEE Transactions on Neural Networks, 2006, 17(2): 432-444.[25] 张国敏, 殷建平, 祝恩, 等. 一种新的多层感知机隐含层神经元个数上限计算方法 [J]. 计算机工程与科学, 2007,29(9): 137-139. ZHANG G M, YIN J P, ZHU E, et al.. A new method of calculating the upper limit on multilayer perception's hidden neuron number[J]. Computer Engineering & Science, 2007, 29(9): 137-139. (in Chinese)[26] SABANOVIC A, ABIDIT K, ELITAS M. A study on high accuracy discrete-time sliding mode control . Proceedings of International Conference on Power Electronics and Motion Control, Portoroz, Slovenia, 2006: 355-360.[27] WEI Q, HU CH Z, ZHANG D. Neural network adaptive control of piezoelectric actuator in Scanning Tunneling Microscope . Proceedings of International Conference on Digital Manufacturing and Automation, Zhangjiajie, China, 2011: 767-771.

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