Magnetic shape memory alloy (MSMA) based actuators are extensively applied in the fields of precision manufacturing and micro/nano technology. Nevertheless, the inherent hysteresis in the MSMA-based actuator severely hinders its further application. In this letter, the characteristics of hysteresis behavior under different input signals are investigated. Then, a nonlinear auto-regressive moving average with exogenous inputs (NARMAX) model based on a diagonal recurrent neural network (DRNN) is used to construct the rate-dependent hysteresis model. To improve the capability of characterizing the multi-valued mapping of the hysteresis loop, the play operator is adopted as the exogenous variable function of the NARMAX model. To verify the effectiveness of the proposed model, a series of comparisons are implemented. The experimental results show that the proposed NARMAX model based on the DRNN exhibits excellent modeling performance.

中文翻译：

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基于 NARMAX 模型的磁性形状记忆合金致动器磁滞建模

基于磁性形状记忆合金（MSMA）的执行器广泛应用于精密制造和微/纳米技术领域。然而，基于 MSMA 的执行器的固有滞后严重阻碍了其进一步应用。在这封信中，研究了不同输入信号下滞后行为的特征。然后，使用基于对角递归神经网络 (DRNN) 的具有外源输入的非线性自回归移动平均 (NARMAX) 模型来构建速率相关滞后模型。为了提高表征滞后环多值映射的能力，采用play算子作为NARMAX模型的外生变量函数。为了验证所提出模型的有效性，实施了一系列比较。