Modeling rate-dependent and thermal-drift hysteresis through preisach model and neural network optimization approach

  • Authors:

  • Shunli Xiao;Yangmin Li

  • Affiliations:

  • Department of Electromechanical Engineering, University of Macau, Macao, China;Department of Electromechanical Engineering, University of Macau, Macao, China,School of Mechanical Engineering, Tianjin University of Technology, Tianjin, China

  • Venue:
  • ISNN'12 Proceedings of the 9th international conference on Advances in Neural Networks - Volume Part I
  • Year:
  • 2012

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Abstract

Smart material actuators like Piezoelectric(PZT) are widely used in Micro/Nano manipulators, but their hysteresis behaviors are complex and difficult to model. Most hysteresis models are based on elementary quasistatic operators and are not suitable for modeling rate-dependent or thermal-drift behaviors of the actuators. This work proposes a Preisach model based neurodynamic optimization model to account for the complex hysteresis behaviors of the smart material actuator system. Through simulation study, the rate-dependent and the thermal-drift behaviors are simulated via Bouc-Wen model. The μ-density function of the Preisach model is identified on-line through neurodynamic optimization method to suit for the varied rate of the input signals. The output of the actuator system is predicated in realtime based on the on-line identified μ-density plane. It is shown experimentally that the predicated hysteresis loops match the simulated PZT loops very well.