Motion Control of a Soft Circular Crawling Robot via Iterative Learning Control

Soft robots have recently attracted widespread attention due to their abilities to work effectively in unstructured environments. As an actuation technology of soft robots, dielectric elastomer actuators (DEAs) exhibit many fantastic attributes such as large strain and high energy density. However, due to nonlinear electromechanical coupling, it is challenging to model a DEA accurately, and further it is difficult to control a DEA-based soft robot. This work studies a novel DEA-based soft circular crawling robot. The kinematics of the soft robot is explored and a knowledge-based model is established to expedite the controller design. An iterative learning control (ILC) method then is applied to control the soft robot. By employing ILC, the performance of the robot motion trajectory tracking can be improved significantly without using a perfect model. Finally, several numerical studies are conducted to illustrate the effectiveness of the ILC.