Precise position control using shape memory alloy wires

Shape memory alloys (SMAs) are active metallic ``smart'' materials used as actuators and sensors in high technology smart systems [1]. The term shape memory refers to ability of certain materials to ``remember'' a shape, even after rather severe deformations: once deformed at low temperatures, these materials will stay deformed until heated, whereupon they will return to their original, pre-deformed ``learned'' shape [2]. This property can be used to generate motion and/or force in electromechanical devices and micro-machines. However, the accuracy of SMA actuators is severely limited by their highly nonlinear stimulus-response characteristics. In this work, modeling, simulation, and experimental efforts to precisely control the position of a Ni-Ti based shape memory alloy wire is presented. In this content three separate control strategies are tried and very good positioning accuracies are obtained.

Precise position control using shape memory alloy wires

Shape memory alloys (SMAs) are active metallic ``smart'' materials used as actuators and sensors in high technology smart systems [1]. The term shape memory refers to ability of certain materials to ``remember'' a shape, even after rather severe deformations: once deformed at low temperatures, these materials will stay deformed until heated, whereupon they will return to their original, pre-deformed ``learned'' shape [2]. This property can be used to generate motion and/or force in electromechanical devices and micro-machines. However, the accuracy of SMA actuators is severely limited by their highly nonlinear stimulus-response characteristics. In this work, modeling, simulation, and experimental efforts to precisely control the position of a Ni-Ti based shape memory alloy wire is presented. In this content three separate control strategies are tried and very good positioning accuracies are obtained.

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  • Number of Hidden Layers Figure 19. Performance index with respect to number of hidden layers. The input layer of the NN receives two successive samples of the desired contraction signal and the output layer gives the estimated value of the necessary current that must be applied to the wire in order to obtain the desired contraction. In the employed NN scheme 1000 iterations have been found necessary to achieve a minimum of the error term.
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