DESIGN OF RECURRENT FUZZY NEURAL NETWORK AND GENERAL REGRESSION NEURAL NETWORK CONTROLLER FOR TRAVELING-WAVE ULTRASONIC MOTOR

Tien-Chi Chen, Tsai-Jiun Ren, Yi-Wei Lou

2011

Abstract

The traveling-wave ultrasonic motor (TWUSM) has significant features such as high holding torque at low speed range, high precision, fast dynamics, simple structure, no electromagnetic interference. The TWUSM has been used in many practical areas such as industrial, medical, robotic, and automotive applications. However, the dynamic model of the TWUSM motor has the nonlinear characteristic and dead-zone problem which varies with many driving conditions. This paper presents a novel control scheme, recurrent fuzzy neural network (RFNN) and general regression neural network (GRNN) controller, for a TWUSM control. The RFNN provides a real-time control such that the TWUSM output can track the reference command. The back-propagation algorithm is applied in the RFNN to automatically adjust the parameters on-line. The adaptive laws of the RFNN are derived by Lyapunov theorem such that the stability of the system can be absolute. The GRNN controller is appended to the RFNN controller to compensate the dead-zone of the TWUSM system using a predefined set. The experimental results are provided to demonstrate the effectiveness of the proposed controller.

References

  1. Sashida, T., Kenjo, T., 1993. An introduction to ultrasonic motors, Clarendon Press, Oxford,
  2. Ueha, S., Tomikawa, Y., 1993. Ultrasonic motors theory and applications, Clarendon Press, Oxford.
  3. Uchino, K., 1997. Piezoelectric actuators and ultrasonic motors, Kluwer Academic Publishers.
  4. Huafeng, L., Chunsheng, Z., Chenglin, G., 2005. Precise position control of ultrasonic motor using fuzzy control with dead-zone compensation. J. of Electrical Engineering, vol. 56, no. 1-2, pp. 49-52.
  5. Uchino, K., 1998. Piezoelectric ultrasonic motors: overview. Smart Materials and Structures, vol. 7, pp. 273-285.
  6. Chen, T. C., Yu, C. H., Tsai, M. C., 2008. A novel driver with adjustable frequency and phase for travelingwave type ultrasonic motor. Journal of the Chinese Institute of Engineers, vol. 31, no. 4, pp. 709-713.
  7. Hagood, N. W., Mcfarland, A. J., 1995. Modeling of a piezoelectric rotary ultrasonic motor. IEEE Trans. on Ultrasonics, Ferroelectrics, and Frequency control, vol. 42, no. 2, pp. 210-224.
  8. Bal, G., Bekiroglu, E., 2004. Servo speed control of traveling-wave ultrasonic motor using digital signal processor. Sensor and Actuators A 109, pp. 212-219.
  9. Bal, G., Bekiroglu, E., 2005. A highly effective load adaptive servo drive system for speed control of traveling-wave ultrasonic motor. IEEE Trans. on Power Electronics, vol. 20, no. 5, pp. 1143-1149.
  10. Alessandri, A., Cervellera, C., Sanguineti, M., 2007. Design of asymptotic estimators: an approach based on neural networks and nonlinear programming. IEEE Trans. on Neural Networks, vol. 18, no. 1, pp. 86-96.
  11. Liu, M., 2007. Delayed standard neural network models for control systems. IEEE Trans. on Neural Networks, vol. 18, no. 5, pp. 1376-1391.
  12. Abiyev, R. H., Kaynak, O., 2008. Fuzzy Wavelet Neural Networks for Identification and Control of Dynamic Plants-A Novel Structure and a Comparative Study. IEEE Trans. on Industrial Electronics, vol. 55, no.8, pp. 3133-3140.
  13. Lin, C. M., Hsu, C. F., 2005. Recurrent neural network based adaptive -backstepping control for induction servomotors. IEEE Trans. on Industrial Electronics, vol. 52, no. 6, pp. 1677-1684.
  14. Ku C. C., Lee, K. Y., 1995. Diagonal recurrent neural networks for dynamic systems control. IEEE Trans. on Neural Networks, vol. 6, no. 1, pp. 144-156.
  15. Juang, C. F., Huang, R. B., Lin, Y. Y., 2009. A Recurrent Self-Evolving Interval Type-2 Fuzzy Neural Network for Dynamic System Processing. IEEE Trans. on Fuzzy Systems, vol. 17, no. 5, pp. 1092-1105.
  16. Stavrakouds, D. G., Theochairs, J. B., 2007. Pipelined Recurrent Fuzzy Neural Networks for Nonlinear Adaptive Speech Prediction. IEEE Trans. on Systems, Man and Cybernetics, Part B, vol. 37, no. 5, pp. 1305- 1320.
  17. Lin, C. J., Chen, C. H., 2005. Identification and prediction using recurrent compensatory neuro-fuzzy systems. Fuzzy Sets and Systems, vol. 150, no. 2, pp. 307-330.
  18. Senjyu, T., Kashiwagi, T., Uezato, K., 2002. Position control of ultrasonic motors using MRAC with deadzone compensation. IEEE Trans. on Power Electronics, vol. 17, no. 2, pp. 265-272.
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Paper Citation


in Harvard Style

Chen T., Ren T. and Lou Y. (2011). DESIGN OF RECURRENT FUZZY NEURAL NETWORK AND GENERAL REGRESSION NEURAL NETWORK CONTROLLER FOR TRAVELING-WAVE ULTRASONIC MOTOR . In Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: NCTA, (IJCCI 2011) ISBN 978-989-8425-84-3, pages 31-40. DOI: 10.5220/0003677800310040


in Bibtex Style

@conference{ncta11,
author={Tien-Chi Chen and Tsai-Jiun Ren and Yi-Wei Lou},
title={DESIGN OF RECURRENT FUZZY NEURAL NETWORK AND GENERAL REGRESSION NEURAL NETWORK CONTROLLER FOR TRAVELING-WAVE ULTRASONIC MOTOR},
booktitle={Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: NCTA, (IJCCI 2011)},
year={2011},
pages={31-40},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003677800310040},
isbn={978-989-8425-84-3},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: NCTA, (IJCCI 2011)
TI - DESIGN OF RECURRENT FUZZY NEURAL NETWORK AND GENERAL REGRESSION NEURAL NETWORK CONTROLLER FOR TRAVELING-WAVE ULTRASONIC MOTOR
SN - 978-989-8425-84-3
AU - Chen T.
AU - Ren T.
AU - Lou Y.
PY - 2011
SP - 31
EP - 40
DO - 10.5220/0003677800310040