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Position control of Shape Memory Alloy (SMA) actuators has been a challenging topic during the last years due to their nonlinearities in the governing physical equations as well as their hysteresis behaviors. Using the inverse of phenomenological hysteresis model in order to compensate the input-output hysteresis behavior of these actuators shows the effectiveness of this approach. In this paper, in order to control the tip deflection of a large deformation flexible beam actuated by an SMA actuator wire, a feedforward-feedback controller is proposed. The feedforward part of the proposed control system, maps the beam deflection into SMA temperature, is based on the inverse of the generalized... 

Among the phenomenological hysteresis models, the Preisach model, Krasnosel'skii-Pokrovskii model, and Prandtl-Ishlinskii model have found extensive applications for modeling hysteresis in shape memory alloys and other smart actuators. Since the mathematical complexity of the identification and inversion problem depends directly on the type of phenomenological hysteresis modeling method, choosing a proper phenomenological model among the mentioned models for modeling the hysteretic behavior of shape memory alloy actuators is a task of crucial importance. Moreover, the accuracy of the hysteresis modeling method in characterizing shape memory alloy hysteretic behavior consequently affects the... 

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... 

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... 

Hysteresis and significant nonlinearities in the behavior of Shape Memory Alloy (SMA) actuators encumber effective utilization of these actuator. Due to these effects, the position control of SMA actuators has been a great challenge in recent years. Literature review of the research conducted in this area shows that using the inverse of the phenomenological hysteresis models can compensate the hysteresis of these actuators effectively. But, inverting some of these models, such as Preisach model, is numerically a complex task. However, the generalized Prandtl-Ishlinskii model is analytically invertible, and therefore can be implemented conveniently as a feedforward controller for compensating...