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A novel preisach based neural network approach to hysteresis non-linearity modeling

Firouzi, M ; Sharif University of Technology | 2010

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  1. Type of Document: Article
  2. Publisher: 2010
  3. Abstract:
  4. In some systems with hysteresis behavior like Shape Memory Alloy (SMA) actuators and Piezo actuators, we essentially need an accurate modeling of hysteresis either for controller design or performance evaluation. One of the most interesting Hysteresis non-linearity identification methods is Preisach model in which hysteresis is modeled by linear combination of elemental operators. Despite good ability of Preisach modeling to extract main features of system with hysteresis behavior, cause of tough numerical nature of Preisach, it is not convenient to use in real-time control applications. In this paper we present a novel method based on Artificial Neural Network. For evaluation of proposed approach we use experimental apparatus consists of one-dimensional flexible aluminum structure with SMA wire as deflection controller actuator which has hysteresis characteristic
  5. Keywords:
  6. Artificial neural networks (ANNs) ; Shape Memory Alloy (SMA) ; Accurate modeling ; Aluminum structures ; Controller designs ; Experimental apparatus ; Hysteresis behavior ; Hysteresis characteristics ; Hysteresis modeling ; Identification method ; Linear combinations ; Non-Linearity ; Performance evaluation ; Piezo actuator ; Preisach ; Preisach model ; Preisach modeling ; Shape memory alloy actuators ; SMA wire ; Actuators ; Intelligent materials ; Neural networks ; Real time control ; Shape memory effect ; Hysteresis
  7. Source: Proceedings of the 2010 International Conference on Artificial Intelligence, ICAI 2010, 12 July 2010 through 15 July 2010, Las Vegas, NV ; Volume 1 , 2010 , Pages 299-305 ; 9781601321480 (ISBN)
  8. URL: https://pdfs.semanticscholar.org/904d/5084c37d6745d877d5f5a15e6dc702258c31.pdf