Kinematic Modeling of Twisted String Actuator Based on Invertible Neural Networks

Twisted String Actuators (TSAs) exhibit several advantages, including lightweight, compact, and having a high power-to-weight ratio. However, current research on kinematic models of TSAs is limited to deriving the relationship between motor input and output through idealized geometric calculations. Therefore, the accuracy of these models does not meet the requirements for practical applications. Previous studies on the kinematic modeling of TSA have not considered the impact of material plastic deformation and stroke times on TSA kinematics. Accumulation of plastic deformation over multiple strokes leads to changes in the output displacement of TSA, significantly affecting the accuracy of the kinematic model. This study aims to address the limitations of previous research by investigating the use of Invertible Neural Networks (INNs) in kinematic modeling of TSAs, taking into account material plastic deformation and stroke times. Through a series of TSA experiments, a kinematic model of TSA was established using an INN that considers stroke times. The INN model proves to be superior in both forward and inverse kinematic modeling by effectively compensating for the effects of plastic deformation during TSA operation. The experimental results demonstrate that the kinematic model established by the proposed INN is more aligned with the actual conditions when compared to traditional kinematic models. This insight can aid in predicting the lifespan of TSA in the future.