Constant Fluidic Mass Control for Soft Actuators Using Artificial Neural Network Algorithm

Soft fluidic actuators are increasingly being used for wearable haptic devices due to their high energy density and low encumbrance. These actuators are typically controlled using constant fluidic pressure control (CFPC), where the actuator pressure is switched between a high pressure source and atmospheric pressure using a fluidic valve. However, this type of control has several limitations for soft actuators including limited dynamic range, slow actuator response, low pressure control resolution and unnatural haptic interaction. In this paper, we present a novel control strategy for soft fluidic actuators, called constant fluidic mass control (CFMC), where the mass of fluid introduced into the actuator is kept constant during actuation, rather than the pressure as in CFPC. Our experimental results show that compared to CFPC, CFMC results in a larger dynamic range of actuator output forces, faster actuator response time to reach a desired target pressure, and higher resolution of pressure control, which makes it particularly useful for wearable haptics. In addition, CFMC enables analog pressure control and we present a neural-network-based supervised learning algorithm for accurate pressure control of soft actuators. Results show that our algorithm can predict actuator pressure with an accuracy of 99% and can be generalized to different soft TPU-fabric fluidic actuators.