Optimization of the recursive least squares algorithm for capacitive strain sensing

Abstract

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.