Addressing Viscoelastic Effects in the Control of Soft Robotic Arm by Combining Spring-Damper Models and Recurrent Neural Networks

Soft robotic arms based on flexible materials or structures have gained popularity in recent years due to their superior adaptability and safety. However, controlling soft arms faces challenges due to various nonlinear problems, such as vis-coelastic effects. The viscoelastic effects can be categorized into dynamic viscoelasticity, which occurs in short term (e.g. about 10 minutes), and steady-state viscoelasticity, which manifests over longer periods (e.g. more than one hour). Although a small number of studies have explored the viscoelastic properties of soft robots before, there is still limited research on the control of soft arms taking into account viscoelastic effects, especially the steady-state viscoelastic effects. This paper proposes a method that combines modeling and learning to address the impact of dynamic and steady-state viscoelasticity on soft robot control. This paper introduces a method for separating between dynamic and steady-state viscoelastic effects and establishes a model for steady-state viscoelasticity. By integrating this model with a recurrent neural network (RNN), we achieve stable and accurate open-loop control of the flexible arm. Experimental results demonstrate that the RNN effectively compensates for the influence of historical motion states on dynamic viscoelasticity. Moreover, the incorporation of the steady-state viscoelastic model enables the controller to maintain high accuracy in open-loop control experiments for up to five hours.