Hysteresis as a Feature, not a Bug—Exploiting Textile Hysteresis for Wearable Soft Robots

Pneumatic textile soft actuators have shown significant promise for wearable assistive robots due to their ability to be easily integrated into apparel. One of the key challenges for controlling these devices, however, is the large amount of mechanical hysteresis they exhibit due to friction between the yarns of the fabric. In this work, we propose a phenomenological modeling approach to capture these effects based on techniques originally developed for magnetic systems. These modeling approaches have been shown to be highly versatile for a range of physical systems, in particular those that exhibit "return point memory"—a trait found in the mechanical response of textiles. Conventionally, the goal of such a hysteresis model would be to "cancel-out" the hysteretic effects when controlling a system. While we do demonstrate that our model is very effective at this task, we also go further by demonstrating that the hysteresis can actually be exploited to increase the performance of the wearable device. Specifically, by carefully controlling the loading sequence of the actuator, we can reach hysteretic states that fully support the user's body with reduced actuator air pressure, increasing perceived comfort and system bandwidth.