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Inverse Hysteresis Modeling for Piezoelectric Stack Actuators with Inverse Generalized Prandtl-Ishlinskii Model

Inverse Generalized Prandtl-Ishlinskii Model를 이용한 압전 스택 액추에이터의 역 히스테리시스 모델링

  • Ko, Young-Rae (School of Mechanical Engineering, Chung-Ang University) ;
  • Kim, Tae-Hyoung (School of Mechanical Engineering, Chung-Ang University)
  • Received : 2014.03.05
  • Accepted : 2014.03.26
  • Published : 2014.04.25

Abstract

Piezoelectric actuators have been widely used in various applications because they have many advantages such as fast response time, repeatable nanometer motion, and high resolution. However Piezoelectric actuators have the strong hysteresis effect. The hysteresis effect can degrade the performance of the system using piezoelectric actuators. In past study, the parameters of the inverse hysteresis model are computed from the identified parameters using the Generalized Prandtl-Ishlinskii(GPI) model to cancel the hysteresis effect, however according to the identified parameters there exist the cases that can't form the inverse hysteresis loop. Thus in this paper the inverse hysteresis modeling mothod is proposed using the Inverse Generalized Prandtl-Ishlinskii(IGPI) model to handle that problem. The modeling results are verified by experimental results using various input signals.

압전 액추에이터(Piezoelectric actuator)는 빠른 응답 특성, 넓은 대역폭, 우수한 반복 정밀도, 그리고 높은 분해능의 특성으로 인하여 다양한 산업분야에서 폭넓게 사용되고 있다. 하지만, 압전 액추에이터에는 히스테리시스 효과(Hysteresis effect)가 발생되는 단점이 있으며, 이는 시스템의 성능을 저하시키는 주요한 원인으로 알려져 있다. Generalized Prandtl-Ishlinskii(GPI) model을 이용한 기존 연구에서는 히스테리시스 효과를 제거하기 위하여 히스테리시스를 수리적으로 모델링하고, 그 결과로부터 역 히스테리시스를 도출하였다. 하지만 모델링된 변수 값에 따라서는 역 히스테리시스 루프를 형성하지 못하는 치명적 문제점이 발생된다. 따라서 본 논문에서는 이러한 문제점을 해결하기 위하여 Inverse Generalized Prandtl-Ishlinskii(IGPI) model을 이용하여 역 히스테리시스를 직접 모델링하는 방법을 제안하였다. 또한 모델링 정밀도는 다양한 입력신호를 이용한 실험 결과를 기반으로 검증하였다.

Keywords

References

  1. S. Devasia, E. Eleftheriou, and S. O. R. Moheimani, "A survey of control issues in nanopositioning," IEEE Transactions on Control Systems Technology, Vol. 15, No. 5, pp. 802-823, 2007. https://doi.org/10.1109/TCST.2007.903345
  2. G.-Y. Gu, L.-M. Zhu, C.-Y. Su, and H. Ding, "Motion control of piezoelectric positioning stages: modeling, controller design and experimental evaluation," IEEE/ASME Transactions on Mechatronics, 2012.
  3. Y. Chen, J. Qiu, J. Palacios, and E. C. Smith, "Tracking control of piezoelectric stack actuator using modified Prandtl-Ishlinskii model," Journal of Intelligent Material Systems and Structures, Vol. 24, No. 6, pp. 753-760, 2013. https://doi.org/10.1177/1045389X12455725
  4. M. Al Janaideh, S. Rakheja, and C.-Y. Su, "A generalized Prandtl-Ishlinskii model for characterizing the hysteresis and saturation nonlinearities of smart actuators," Smart Materials and Structures, Vol. 18, no. 4, 2009.
  5. M.-J. Yang, G.-Y. Gu, and L.-M. Zhu, "Parameter identification of the generalized Prandtl-Ishlinskii model for piezoelectric actuators using modified particle swarm optimization," Sensors and Actuators A: Physical, Vol. 189, pp. 254-265, 2013. https://doi.org/10.1016/j.sna.2012.10.029
  6. Q. Xu and Y. Li, "Dahl model-based hysteresis compensation and precise positioning control of an XY parallel micromanipulator with piezoelectric actuation," Journal of Dynamic Systems, Measurement, and Control 132 (4),Vol. 132, 2010.
  7. M. Al Janaideh, S. Rakheja, and C.-Y. Su, "An analytical generalized Prandtl-Ishlinskii model inversion for hysteresis compensation in micropositioning control," IEEE/ASME Trans Mech, 2011.
  8. H. W. Beak, T.-H. Kim, J. N. Ryu, J. I. Oh, "Diversity-enhanced particle swarm optimizer and its application to optimal flow control of sewer networks," Science and Information Conference, 2013.
  9. H. Sayyaadi and M. R. Zakerzadeh, "Position control of shape memory alloy actuator based on the generalized Prandtl-Ishlinskii inverse model," Mechatronics, pp. 945-957, 2013.
  10. K. Kuhnen and H. Janocha, "Inverse feedforward controller for complex hysteretic nonlinearities in smart-material systems," Control Intelligence system, Vol. 29, pp. 74-83, 2001.
  11. H. W. Beak, T.-H. Kim, J. N. Ryu, J. I. Oh, "Model Predictive Control for Distributed Storage Facilities and Sewer Network Systems via PSO," Journal of Korean Institute of Intelligent Systems, Vol. 22, No. 6, pp. 722-728, 2012. https://doi.org/10.5391/JKIIS.2012.22.6.722
  12. S.-Y. Lee, M. Park, and J. H. Baek, "Modeling of Dynamic Hysteresis Based on Takagi-Sugeno Fuzzy Duhem Model," International Journal of Fuzzy Logic and Intelligent Systems, Vol. 13, No. 4, pp. 277-283, 2013. https://doi.org/10.5391/IJFIS.2013.13.4.277

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