Implementing smart materials as an actuator in fabricating micro-positioning systems has become pervasive in recent years. However, the application of Shape Memory Alloy (SMA) smart materials is limited due to its complex nonlinear mechanical behavior, such as asymmetric hysteresis and saturation characteristics. One of the most potent experimental-based methods of modeling these nonlinearities is the Generalized Prandtl-Ishlinskii (GPI) model. Unlike similar methods such as the Preisach model, this model is analytically invertible. This study aims to develop a micro-positioning stage and identify an experimental-based model describing the system response. The model structure is composed of two cascade sub-models. In the first sub-model, which models the actuator thermal behavior, the parameters of a linear dynamic model are identified. This sub-model predicts the actuator temperature given the electrical current. The second sub-model estimates the phase transformation and consequently the actuator displacement as a function of temperature. The GPI structure has been used for constructing the Wiener sub-model. The experimental and numerical results showed that the proposed black box model can accurately describe the system behavior, although identifying a comprehensive model to adequately describe the SMA actuator is a great challenge.

近年来，将智能材料用作制造微定位系统的执行器已变得普遍。然而，形状记忆合金（SMA）智能材料由于其复杂的非线性力学行为，如不对称磁滞和饱和特性，其应用受到限制。对这些非线性进行建模的最有效的基于实验的方法之一是广义普朗特-伊什林斯基 (GPI) 模型。与 Preisach 模型等类似方法不同，该模型在分析上是可逆的。本研究旨在开发微定位平台并确定描述系统响应的基于实验的模型。该模型结构由两个级联子模型组成。在对执行器热行为进行建模的第一个子模型中，确定了线性动态模型的参数。该子模型预测给定电流的执行器温度。第二个子模型估计相变，从而估计执行器位移作为温度的函数。GPI 结构已用于构建维纳子模型。实验和数值结果表明，尽管确定一个综合模型来充分描述 SMA 执行器是一个巨大的挑战，但所提出的黑盒模型可以准确地描述系统行为。