Loading...

Hysteresis nonlinearity identification by using RBF neural network approach

Firouzi, M ; Sharif University of Technology

882 Viewed
  1. Type of Document: Article
  2. DOI: 10.1109/IRANIANCEE.2010.5506985
  3. Abstract:
  4. In systems with hysteresis behavior like magnetic cores, Piezo actuators, Shape Memory Alloy(SMA), we essentially need an accurate modeling of hysteresis either for design or performance evaluation; also in some control applications accurate system identification is needed. One of the famous methods of Hysteresis modeling is Preisach model. In this numerical method hysteresis is modeled by linear combination of smaller hysteresis loops as an elemental operator and local memory. In this paper we discuss those Radial Base artificial neural networks (RBF) which provides natural settings in accordance with the Preisach model. It is shown that the proposed approach can represent hysteresis modeling accurately in compare with classical Preisach model and can be used for many applications such as hysteresis nonlinearity control, hysteresis identification and realization for performance evaluation and system design. For evaluation we use measured experimental data from hysteresis SMA wire as an actuator
  5. Keywords:
  6. Radial base function artificial neural networks ; Shape memory alloy(SMA) ; Artificial neural network ; Hysteresis identification ; Preisach model ; Radial base function ; Shape memory alloy ; Actuators ; Alloys ; Electrical engineering ; Hysteresis ; Hysteresis loops ; Identification (control systems) ; Magnetic circuits ; Magnetic materials ; Mathematical models ; Radial basis function networks ; Shape memory effect ; Neural networks
  7. Source: Proceedings - 2010 18th Iranian Conference on Electrical Engineering, ICEE 2010, 11 May 2010 through 13 May 2010 ; 2010 , Pages 692-697 ; 9781424467600 (ISBN)
  8. URL: http://ieeexplore.ieee.org/document/5506985