In systems with hysteresis behavior like Shape Memory Alloy (SMA) actuators and Piezo actuators, an accurate modeling of hysteresis behavior either for performance evaluation and identification or controller design is essentially needed. One of the most interesting hysteresis none-linearity identification methods is Preisach model which the hysteresis is modeled by linear combination of hysteresis operators. In spite of good ability of the Preisach model to extract the main features of system with hysteresis behavior, due to its numerical nature, it is not convenient to use in real time control applications. In this paper a novel artificial neural network (ANN) approach based on the Preisach model is presented which provides accurate hysteresis none-linearity modeling. It is shown that the proposed approach can represent hysteresis behavior more accurately in compare with the classical Preisach model and can be used for many applications such as hysteresis non-linearity control, hysteresis identification and realization for performance evaluation in some physical systems such as magnetic and SMA materials. It is also greatly decrease the extremely large amount of calculation needed to numerically implement the Preisach hysteresis model. For evaluation of the proposed approach an experimental apparatus consists of one-dimensional flexible aluminum beam actuated with a SMA wire is used. It is shown that the proposed ANN based Preisach model can identify hysteresis none-linearity more accurately than the classical Preisach model besides to its reduction in the simulation and computation time.
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Hysteresis Identification of Shape Memory Alloy Actuators Using a Novel Artificial Neural Network Based Presiach Model
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Zakerzadeh, MR, Firouzi, M, Sayyaadi, H, & Shouraki, SB. "Hysteresis Identification of Shape Memory Alloy Actuators Using a Novel Artificial Neural Network Based Presiach Model." Proceedings of the ASME 2010 Conference on Smart Materials, Adaptive Structures and Intelligent Systems. ASME 2010 Conference on Smart Materials, Adaptive Structures and Intelligent Systems, Volume 1. Philadelphia, Pennsylvania, USA. September 28–October 1, 2010. pp. 653-660. ASME. https://doi.org/10.1115/SMASIS2010-3903
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