This paper describes the development of fuzzy systems for modeling the hysteresis behavior of shape memory alloy (SMA) actuators. Due to their simplicity and ease of actuation, SMA actuators are very attractive for applications such as miniature robots for micro manufacturing. However, SMAs have not been widely used for motion control applications due to their nonlinear behavior and control difficulties. One approach to design a position controller for SMA systems is to employ an inverse-model of the system in the control loop to compensate the hysteresis properties of the material. Fuzzy systems, due to their nonlinear learning and adaptation abilities, are good candidates for obtaining inverse-models. In this paper two fuzzy modeling approaches are employed and compared to develop a model for a SMA wire actuator. A set of experiments are conducted to generate the training data. The test stand includes a Nickel-Titanium (TiNi) SMA wire, a position sensor, a bias spring and a current amplifier. By comparing the performance of the two employed fuzzy modeling techniques, it is revealed that the approach based on fuzzy Gustafson-Kessel (GK) clustering shows a better performance in the modeling of the hysteresis in the SMA wire. Thus, GK clustering algorithm is employed to develop the inverse-model for the SMA. The reported results demonstrate the ability of the employed fuzzy algorithm for modeling the hysteresis in the system, and the merits of the introduced inverse-model in the control of the position of the SMA.

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