Acta Mechanica Solida Sinica

, Volume 25, Issue 4, pp 417–428 | Cite as

Vibration Suppression of a Flexible Piezoelectric Beam Using BP Neural Network Controller

  • Zhicheng Qiu
  • Xiangtong Zhang
  • Chunde Ye


This paper aims at modeling and developing vibration control methods for a flexible piezoelectric beam. A collocated sensor/actuator placement is used. Finite element analysis (FEA) method is adopted to derive the dynamics model of the system. A back propagation neural network (BPNN) based proportional-derivative (PD) algorithm is applied to suppress the vibration. Simulation and experiments are conducted using the FEA model and BPNN-PD control law. Experimental results show good agreement with the simulation results using finite element modeling and the neural network control algorithm.

Key words

flexible piezoelectric beam active vibration control neural network finite element analysis 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. [1]
    Yang, T.W., Xu, W.L. and Han, J.D., Dynamic compensation control of flexible macro-micro manipulator systems. IEEE Transactions on Control System Technology, 2010, 18(1): 143–151.CrossRefGoogle Scholar
  2. [2]
    Peng, X.Q., Lam, K.Y. and Liu, G.R., Active vibration control of composite beams with piezoelectrics: a finite element model with third order theory. Journal of Sound and Vibration, 1998, 209(4): 635–649.CrossRefGoogle Scholar
  3. [3]
    Hurlebaus, S. and Gaul, L., Smart structure dynamics. Mechanical Systems and Signal Processing, 2006, 20(2): 255–281.CrossRefGoogle Scholar
  4. [4]
    Xu, S.X. and Koko, T.S., Finite element analysis and design of actively controlled piezoelectric smart structures. Finite Elements in Analysis and Design, 2004, 40(3): 241–262.CrossRefGoogle Scholar
  5. [5]
    Janocha, H., Adaptronics and Smart Structures. Berlin Heidelberg: Springer-Verlag, 2007.CrossRefGoogle Scholar
  6. [6]
    Bandyopadhyay, B., Manjunath, T.C. and Umapathy, M., Modeling, Control and Implementation of Smart Structures: a FEM State-space Approach. Berlin Heidelberg: Springer-Verlag, 2007.zbMATHGoogle Scholar
  7. [7]
    Lee, G.S., System identification and control of smart structures using neural networks. Acta Astronautica, 1996, 38(4–8): 269–276.CrossRefGoogle Scholar
  8. [8]
    Kawabe, H., Tsukiyama, N. and Yoshida, K., Active vibration damping based on neural network theory. Materials Science and Engineering A—Structural Materials Properties Microstructure and Processing, 2006, 442(1–2): 547–550.CrossRefGoogle Scholar
  9. [9]
    Tian, L.F. and Collins, C., Adaptive neuro-fuzzy control of a flexible manipulator. Mechatronics, 2005, 15(10): 1305–1320.CrossRefGoogle Scholar
  10. [10]
    Shi, Y.M., Hua, H.X. and Sol, H., The finite element analysis and experimental study of beams with active constrained layer damping treatments. Journal of Sound and Vibration, 2004, 278(1–2): 343–363.MathSciNetCrossRefGoogle Scholar
  11. [11]
    Mohamed, Z. and Tokhi, M.O., Command shaping techniques for vibration control of a flexible robot manipulator. Mechatronics, 2004, 14(1): 69–90.CrossRefGoogle Scholar
  12. [12]
    Kattan, P.L., MATLAB Guide to Finite Elements. Berlin Heidelberg: Springer-Verlag, 2008.CrossRefGoogle Scholar
  13. [13]
    Xie, Y., Zhao, T. and Cai, G.P., Model reduction and active control for a flexible plate. Acta Mechanica Solida Sinia, 2011, 24(5): 467–476.CrossRefGoogle Scholar
  14. [14]
    Gupta, V., Sharma, M. and Thakur, N., Optimization criteria for optimal placement of piezoelectric sensors and actuators on a smart structure: a technical review. Journal of Intelligent Materials Systems and Structures, 2010, 21(12): 1227–1243.CrossRefGoogle Scholar
  15. [15]
    Liu, J.K., Matlab Simulation of Advanced PID Control. Beijing: Publishing House of Electronics Industry, 2004 (in Chinese).Google Scholar
  16. [16]
    Wei, J.J., Qiu, Z.C., Han, J.D. and Wang, Y.C., Experimental comparison research on active vibration control for flexible piezoelectric manipulator using fuzzy controller. Journal of Intelligent and Robotic Systems, 2010, 59(1): 31–56.CrossRefGoogle Scholar

Copyright information

© The Chinese Society of Theoretical and Applied Mechanics and Technology 2012

Authors and Affiliations

  1. 1.School of Mechanical and Automotive EngineeringSouth China University of TechnologyGuangzhouChina

Personalised recommendations