Identification of Ultrasonic Motor’s Nonlinear Hammerstein Model

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• 作者：Shi Jingzhuo ; Zhao Juanping ; Huang Jingtao…
• 关键词：Nonlinear model ; Hammerstein model ; Particle swarm optimization ; Ultrasonic motor
• 刊名：Journal of Control, Automation and Electrical Systems
• 出版年：2014
• 出版时间：October 2014
• 年：2014
• 卷：25
• 期：5
• 页码：537-546
• 全文大小：576 KB
• 参考文献：1. Al-Dhaifllah, M., & Westwick, D. T. (2013). Identification of auto-regressive exogenous hammerstein models based on support vector machine regression. / IEEE Transactions on Control Systems Technology, / 21(6), 2083-090. CrossRef
  2. Bigdeli, N., & Haeri, M. (2004). ‘Modelling of an ultrasonic motor based on Hammerstein model structure- In Proceedings of the International Conference on Control, Automation, Robotics and Vision (vol. 2, pp. 1374-378). Kunming.
  3. Chen, T. C., & Yu, C. H. (2009). Generalized regression neural-network-based modelling approach for traveling-wave ultrasonic motors. / Electric Power Components and Systems, / 37(6), 645-57. CrossRef
  4. Chen, W., Liu, Y., Yang, X., & Liu, J. (2014). Ring-type traveling wave ultrasonic motor using a radial bending mode. / IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, / 61(1), 197-02. CrossRef
  5. Faajeng, L., Kung, Y.-S., Syuanyi, C., & Liu, Y.-H. (2010a). Recurrent wavelet-based Elman neural network control for multi-axis motion control stage using linear ultrasonic motors. / IET Electric Power Applications, / 4(5), 314-32. CrossRef
  6. Faajeng, L., Yingchih, H., & Syuanyi, C. (2010b). Field-programmable gate array-based intelligent dynamic sliding-mode control using recurrent wavelet neural network for linear ultrasonic motor. / IET Control Theory and Applications, / 4(9), 1511-532. CrossRef
  7. Hou, X. Y., Lee, H. P., Lim, S. P., & Ong, C. J. (2013). Numerical study on the performance enhancement of ultrasonic motors using single crystalline piezo-materials. / IEEE Transactions on Magnetics, / 49(6), 2447-450. CrossRef
  8. Li, C. C., Yang, M., Pang, Y. F., & Li, S. Y. (2013). Exciting electrode optimization of a piezoceramic plate type ultrasonic motor by combining artificial immune algorithm and finite element analysis. / Engineering Computations, / 30(5), 751-68. CrossRef
  9. Odomari, S., Hieu, N. T., Uchida, K., Senjyu, T., & Yona, A. (2011). Robust position control for ultrasonic motor using variable structure system observer in non-linear observer. / Electric Power Components and Systems, / 39(16), 1769-782. CrossRef
  10. Ramezanian, H., Balochian, S., & Zare, A. (2013). Design of optimal fractional-order PID controllers using particle swarm optimization algorithm for automatic voltage regulator (AVR) system. / Journal of Control, Automation and Electrical Systems, / 24(5), 601-11. CrossRef
  11. Rasouli, M., Westwick, D., & Rosehart, W. (2014). Quasiconvexity analysis of the Hammerstein model. / Automatica, / 50(1), 277-81. CrossRef
  12. Ratnaweera, A., Halgamuge, S. K., & Watsom, H. C. (2004). Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients. / IEEE Transactions on Evolutionary Computation, / 8(3), 240-55. CrossRef
  13. Ro, J. S., Yi, K. P., Chung, T. K., & Jung, H. K. (2013). Characteristic analysis of an traveling wave ultrasonic motor using a cylindrical dynamic contact model. / Journal of Electrical Engineering and Technology, / 8(6), 1415-423. CrossRef
  14. Sarhadi, P., Ghahramani, N. O., & Shafeenejhad, I. (2013). Identification of a non-linear hydraulic actuator considering rate saturation using particle swarm optimisation algorithm. / International Journal of Modelling, Identification and Control, / 18(2), 136-45. CrossRef
  15. Senjyu, T., Nakamura, M., Urasaki, N., et al. (2008). Mathematical model of ultrasonic motors for speed control. / Electric Power Components and Systems, / 36(6), 637-48. CrossRef
  16. Shi, J., & Liu, B. (2011). Optimum efficiency control of traveling-wave ultrasonic motor system. / IEEE Transactions on Industrial Electronics, / 58(10), 4822-829. CrossRef
  17. Shi, J., & You, D. (2014). Characteristic model of travelling wave ultrasonic motor. / Ultrasonics, / 54(2), 725-30. CrossRef
  18. Wakui, S., & Nakagawa, R. (2011). Consideration about parameter identification for equivalent circuit of ultrasonic motor with two ports. / Transactions of the Japan Society of Mechanical Engineers, Part C, / 77(778), 2259-268. CrossRef
  19. Wan, Z., & Hu, H. (2014). Modeling and experimental analysis of the linear ultrasonic motor with in-plane bending and longitudinal mode. / Ultrasonics, / 54(3), 921-28.
  20. Zhang, X. L., & Tan, Y. H. (2008). Modelling of ultrasonic motor with dead-zone based on Hammerstein model structure. / Journal of Zhejiang University Science A, / 9(1), 58-4. CrossRef
  
• 作者单位：Shi Jingzhuo (1)
  Zhao Juanping (1)
  Huang Jingtao (1)
  Xu Meiyu (1)
  Zhang Juwei (1)
  Zhang Lei (1)
  
  1. School of Electrical Engineering, Henan University of Science and Technology, No. 263 KaiYuan DaDao St., LuoYang?, 471023, China
  
• ISSN：2195-3899

文摘

Ultrasonic motor (USM) has heavy nonlinearity, and its nonlinearity is easily changed along with the change of driving condition. Therefore, it is hard to acquire an accurate model of USM. At present, in the design of USM’s control strategy, an identification method is usually used to establish the model of USM. But the established models are always linear. Because the nonlinearity of USM system is obvious and proper nonlinear model is the foundation of excellent servo control. So in this paper, a kind of modelling method to obtain the nonlinear Hammerstein model of USM’s drive system is proposed. The structure of Hammerstein model consists of a nonlinear static element and a linear dynamic element, which can describe the nonlinearity of USM in a relatively simple form. The modelling method is on the basis of the complexity of USM itself. The parameters of the tested data are identified by using the particle swarm optimization (PSO) method which has good searching ability under complex cases. The proposed model can accurately represent USM model. That is, this PSO method makes it easier and more accurate to produce the model. Simulation results show that the data obtained from the model are close to the actual data; it indicates that the modelling method is reasonable and the established model is valid.