The use of dielectric elastomers as integrated actuators and strain sensors offers a simple approach for closed-loop control in a wide range of applications. While a number of approaches for self-sensing have been proposed, the adaptive online algorithm offers an appealing combination of high accuracy and low computational cost. In this work, the recursive least squares algorithm will be applied to capacitive deformation sensing of dielectric elastomers. With the goal of minimizing sampling rate while achieving a set accuracy over a desired range of deformation frequencies, the probe frequency, sampling frequency, and forgetting factor will be optimized. It will be shown that the accuracy is primarily determined by a nondimensionalized variable, , which defines the proportion of a hypothetical deformation cycle that is weighted more heavily by the algorithm. Ultimately, this optimized algorithm will be validated by variably inflating a dielectric elastomer membrane and comparing the algorithm output to membrane deformation measured by video.

使用介电弹性体作为集成执行器和应变传感器为广泛应用中的闭环控制提供了一种简单的方法。虽然已经提出了许多自感知方法，但自适应在线算法提供了高精度和低计算成本的有吸引力的组合。在这项工作中，递归最小二乘算法将应用于介电弹性体的电容变形传感。为了在所需变形频率范围内实现设定精度的同时最小化采样率，将优化探测频率、采样频率和遗忘因子。将显示精度主要由无量纲化变量决定，，它定义了算法加权更重的假设变形循环的比例。最终，这种优化的算法将通过可变地充气介电弹性体膜并将算法输出与视频测量的膜变形进行比较来验证。