Ultrasonic Motor Control Based on Recurrent Fuzzy Neural Network Controller and General Regression Neural Network Controller

Part of the Studies in Computational Intelligence book series (SCI, volume 465)


The travelling-wave ultrasonic motor (TWUSM) has been used in industrial, medical, robotic and automotive applications. However, the TWUSM has the nonlinear characteristic and dead-zone problem which varies with many driving conditions. A novel control scheme, recurrent fuzzy neural network controller (RFNNC) and general regression neural network controller (GRNNC), for a TWUSM control is presented in this paper. The RFNNC provides real-time control such that the TWUSM output can tightly track the reference command. The adaptive updated RFNNC law is derived using Lyapunov theorem such that the system stability can be absolute. The GRNNC is appended to the RFNNC to compensate for the TWUSM dead-zone using a predefined set. The experimental results are shown to demonstrate the effectiveness of the proposed control scheme.


Travelling-wave ultrasonic motor TWUSM Recurrent fuzzy neural network controller RFNNC Lyapunov theorem General regression neural network controller GRNNC Dead-zone 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Sashida, T., Kenjo, T.: An introduction to ultrasonic motors. Clarendon Press, Oxford (1993)Google Scholar
  2. 2.
    Ueha, S., Tomikawa, Y.: Ultrasonic motors theory and applications. Clarendon Press, Oxford (1993)Google Scholar
  3. 3.
    Uchino, K.: Piezoelectric actuators and ultrasonic motors. Kluwer Academic Publishers (1997)Google Scholar
  4. 4.
    Huafeng, L., Chunsheng, Z., Chenglin, G.: Precise position control of ultrasonic motor using fuzzy control with dead-zone compensation. J. of Electrical Engineering 56(1-2), 49–52 (2005)Google Scholar
  5. 5.
    Uchino, K.: Piezoelectric ultrasonic motors: overview. Smart Materials and Structures 7, 273–285 (1998)CrossRefGoogle Scholar
  6. 6.
    Chen, T.C., Yu, C.H., Tsai, M.C.: A novel driver with adjustable frequency and phase for travelling-wave type ultrasonic motor. Journal of the Chinese Institute of Engineers 31(4), 709–713 (2008)CrossRefGoogle Scholar
  7. 7.
    Hagood, N.W., Mcfarland, A.J.: Modeling of a piezoelectric rotary ultrasonic motor. IEEE Trans. on Ultrasonics, Ferroelectrics, and Frequency Control 42(2), 210–224 (1995)CrossRefGoogle Scholar
  8. 8.
    Bal, G., Bekiroglu, E.: Servo speed control of travelling-wave ultrasonic motor using digital signal processor. Sensor and Actuators A 109, 212–219 (2004)CrossRefGoogle Scholar
  9. 9.
    Bal, G., Bekiroglu, E.: A highly effective load adaptive servo drive system for speed control of travelling-wave ultrasonic motor. IEEE Trans. on Power Electronics 20(5), 1143–1149 (2005)CrossRefGoogle Scholar
  10. 10.
    Alessandri, A., Cervellera, C., Sanguineti, M.: Design of asymptotic estimators: an approach based on neural networks and nonlinear programming. IEEE Trans. on Neural Networks 18(1), 86–96 (2007)CrossRefGoogle Scholar
  11. 11.
    Liu, M.: Delayed standard neural network models for control systems. IEEE Trans. on Neural Networks 18(5), 1376–1391 (2007)CrossRefGoogle Scholar
  12. 12.
    Abiyev, R.H., Kaynak, O.: Fuzzy wavelet neural networks for identification and control of dynamic plants-A novel structure and a comparative study. IEEE Trans. on Industrial Electronics 55(8), 3133–3140 (2008)CrossRefGoogle Scholar
  13. 13.
    Lin, C.M., Hsu, C.F.: Recurrent neural network based adaptive -backstepping control for induction servomotors. IEEE Trans. on Industrial Electronics 52(6), 1677–1684 (2005)CrossRefGoogle Scholar
  14. 14.
    Ku, C.C., Lee, K.Y.: Diagonal recurrent neural networks for dynamic systems control. IEEE Trans. on Neural Networks 6(1), 144–156 (1995)CrossRefGoogle Scholar
  15. 15.
    Juang, C.F., Huang, R.B., Lin, Y.Y.: A recurrent self-evolving interval type-2 fuzzy neural network for dynamic system processing. IEEE Trans. on Fuzzy Systems 17(5), 1092–1105 (2009)CrossRefGoogle Scholar
  16. 16.
    Stavrakouds, D.G., Theochairs, J.B.: Pipelined recurrent fuzzy neural networks for nonlinear adaptive speech prediction. IEEE Trans. on Systems, Man and Cybernetics, Part B 37(5), 1305–1320 (2007)CrossRefGoogle Scholar
  17. 17.
    Lin, C.J., Chen, C.H.: Identification and prediction using recurrent compensatory neuro-fuzzy systems. Fuzzy Sets and Systems 150(2), 307–330 (2005)MathSciNetMATHCrossRefGoogle Scholar
  18. 18.
    Senjyu, T., Kashiwagi, T., Uezato, K.: Position control of ultrasonic motors using MRAC with deadzone compensation. IEEE Trans. on Power Electronics 17(2), 265–272 (2002)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Department of Electrical EngineeringKun Shan UniversityTainanTaiwan
  2. 2.Department of Information EngineeringKun Shan UniversityTainanTaiwan

Personalised recommendations