Improving Perception Accuracy of Flexible Capacitive Stretchable Sensors Using Piecewise Power-Law Model

Flexible capacitive stretchable sensors have a promising prospect in humanoid and soft robots. However, flexible sensors are difficult to be extensively deployed due to their low accuracy caused by intrinsic nonlinearity. The nonlinearity is mainly from the hysteresis of the material of the sensors. In this article, a piecewise power-law (PPL) model is proposed to improve the perception accuracy of a flexible capacitive stretchable sensor. The kernel of the model is the hysteretic operator generated from the PPL function. Benefiting from the design, the proposed model can better describe the asymmetric dynamic behavior of the flexible sensor than some hysteresis models. Its capability to characterize hysteresis in the flexible sensor is demonstrated by comparing the predicted data of the model and the real captured data from the flexible sensor. Then, the proposed model is used to compensate for the hysteresis of the sensor to promote its perception accuracy. The accuracy performance of the proposed method is also compared with the other three different methods. The flexible sensor is deployed in a homemade humanoid knee and a tensile test platform to be tested involving the bending motion and the linear motion, respectively. The experimental results demonstrate that the maximum absolute error (MAE) of the proposed method is significantly smaller than other methods.