A Novel Preisach Based Neural Network Approach to Hysteresis Non-Linearity Modeling
نویسندگان
چکیده
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 onedimensional flexible aluminum structure with SMA wire as deflection controller actuator which has hysteresis characteristic.
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Hysteresis Nonlinearity Identification Using New Preisach Model-Based Artificial Neural Network Approach
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