A proposed soft pneumatic actuator control based on angle estimation from data-driven model

Abstract

This article proposes a bending angle controller for soft pneumatic actuators, which could be implemented in soft robotic rehabilitation gloves to assist patients with hand impairment, such as stroke survivors. A data-driven model is used to estimate the angle as pneumatic pressure is applied to the actuator. Furthermore, a finite element model was used to manually optimize the dimensions of the actuator. An embedded flex sensor, which together with a custom testing rig, was used to gather input data for the data-driven model. This rig contains a pneumatic pressure control circuit as well as a camera for image acquisition. Collected data were fed into a linear regression model to predict the data-driven model. Experiments were carried out to validate model's accuracy as well as modified proportional-integral-derivative controller angle controller performance. The latter controller is designed to mitigate the non-linear response of solenoid valves at different pressures of the actuator. The data-driven model along with the used controller allows more accurate estimation and quicker response.