压电陶瓷驱动平台自适应输出反馈控制

张利军; 杨立新; 郭立东; 孙立宁

doi:10.3724/SP.J.1004.2012.01550

压电陶瓷驱动平台自适应输出反馈控制

doi: 10.3724/SP.J.1004.2012.01550

1.
哈尔滨工程大学自动化学院哈尔滨 150001;
2.
西北工业大学航海学院西安 710072;
3.
黑龙江科技学院电气与信息工程学院哈尔滨 150027;
4.
哈尔滨工业大学机器人研究所哈尔滨 150080

详细信息

通讯作者:
杨立新

计量
- 文章访问数: 1783
- HTML全文浏览量: 78
- PDF下载量: 849
- 被引次数: 0
出版历程
- 收稿日期: 2011-04-13
- 修回日期: 2012-04-09
- 刊出日期: 2012-09-20

Adaptive Output Feedback Control for Piezoactuator-driven Stage

1.
College of Automation, Harbin Engineering University, Harbin 150001;
2.
School of Marine Engineering, Northwestern Polytechnical University, Xi'an 710072;
3.
College of Electrical and Information Engineering, Heilongjiang Institute of Science and Technology, Harbin 150027;
4.
Robotics Institute, Harbin Institute of Technology, Harbin 150080

摘要

摘要: 压电陶瓷驱动平台的精度和动态特性主要取决于所设计的控制器是否可以有效地补偿压电陶瓷固有的迟滞特性. 针对这一问题, 提出了一种基于神经网络 (Neural network, NN)的自适应输出反馈控制策略. 为了避免压电陶瓷速度测量噪声的影响, 采用高增益观测器对压电陶瓷平台的速度状态进行估计; 为了克服压电陶瓷的迟滞非线性特征, 采用神经网络动态补偿策略; 针对神经网络逼近误差和观测器估计误差, 控制器设计中增加了鲁棒控制项. 最后应用Lyapunov 稳定性理论证明了所提出的控制器的收敛性问题. 仿真实验表明了所提控制方法的有效性.
- 输出反馈控制 /
- 压电陶瓷驱动平台 /
- 自适应控制 /
- 神经网络
Abstract: The accuracy and dynamic characteristics of piezoactuator-driven stage mainly depend on whether the controller can effectively compensate the inherent hysteresis of piezoactuator. For this problem, a neural network (NN) based adaptive output feedback control scheme is proposed. In order to avoid the impact of the velocity measurement noise of the piezoactuator, a high-gain observer is used to estimate unmeasured velocity of the system, and a neural network dynamic compensation strategy is proposed. A robust controller is used to compensate the neural network approximation error and the observer estimation error. Finally, the stability analysis is given by Lyapunov stability theory. Simulation results show the effectiveness of the proposed method.
- Output feedback control /
- piezoactuator-driven stage /
- 、adaptive control /
- neural network (NN)

HTML全文

参考文献(1)

[1]

Abidi K, Sabanovic A. Sliding-mode control for high-precision motion of a piezostage. IEEE Transactions on Industrial Electronics, 2007, 54(1): 629-637[2] Ru C H, Sun L N, Rong W B. A control model for hysteresis based on microscopic polarization mechanisms in piezoelectric actuator. Journal of Harbin Institute of Technology, 2008, 15(3): 302-306[3] Xu Q S, Li Y M. Analytical modeling, optimization and testing of a compound bridge-type compliant displacement amplifier. Mechanism and Machine Theory, 2011, 46(2): 183-200[4] Bin Yang, Yang Dong-Chao, Jia Zhen-Zhong, Chen Ken. Study on ECNLP dynamics model of piezoceramic actuator and position tracking controller. Acta Automatica Sinica, 2008, 34(9): 1090-1099(宾洋, 杨东超, 贾振中, 陈恳. 压电陶瓷驱动器ECNLP动力学模型及其位移跟踪控制器的研究. 自动化学报, 2008, 34(9): 1090-1099)[5] Liu Y T, Chang K M, Li W Z. Model reference adaptive control for a piezo-positioning system. Precision Engineering, 2010, 34(1): 62-69[6] Tian Y L, Shirinzadeh B, Zhang D W, Alici G. Development and dynamic modelling of a flexure-based Scott-Russell mechanism for nano-manipulation. Mechanical Systems and Signal Processing, 2009, 23(3): 957-978[7] Kuhnen K. Modeling, identification and compensation of complex hysteretic nonlinearities: a modified Prandtl-Ishlinskii approach. European Journal of Control, 2003, 9(4): 407-418[8] Moheimani S O R, Vautier B J G. Resonant control of structural vibration using charge-driven piezoelectric actuators. IEEE Transactions on Control Systems Technology, 2005, 13(6): 1021-1035[9] Al-Bender F, Lampaert V, Swevers J. The generalized Maxwell-slip model: a novel model for friction simulation and compensation. IEEE Transactions on Automatic Control, 2005, 50(11): 1883-1887[10] Zhang Xin-Liang, Tan Yong-Hong. Neural network model for the dynamic hysteresis based on the expanded input space. Acta Automatica Sinica, 2009, 35(3): 319-323(张新良, 谭永红. 基于输入空间扩张的动态迟滞神经网络模型. 自动化学报, 2009, 35(3): 319-323)[11] Dang X J, Tan Y H. Neural networks dynamic hysteresis model for piezoceramic actuator bases on hysteresis operator of first-order differential equation. Physica B: Condensed Matter, 2005, 365(1-4): 173-184[12] Low T S, Guo W. Modeling of a three-layer piezoelectric bimorph beam with hysteresis. IEEE Journal of Microelectromechanical Systems, 1995, 4(4): 230-237[13] Shieh H J, Hsu C H. An adaptive approximator-based backstepping control approach for piezoactuator-driven stages. IEEE Transactions on Industrial Electronics, 2008, 55(4): 1729-1738[14] Ge S S, Hang C C, Lee T H, Zhang T. Stable Adaptive Neural Network Control. Boston, MA: Kluwer, 2001

施引文献

资源附件(0)

访问统计