Learning Trajectory Tracking for Underactuated Compliant Arms

Trajectory tracking is a classic control theory topic that has received in-depth research in the literature. However, dealing with compliant arms that is underactuated makes the issue more difficult. Compliant systems frequently exhibit difficult-to-model dynamics in addition to their underactuation. To prevent a severe modification of the robot elasticity, the feedback components should be limited. In this letter, we use an iterative learning controller to solve the trajectory tracking problem. The presented control law mixes feedforward and feedback terms. The feedforward component tracks the desired trajectory raising the robot to one equilibrium, and the feedback term stabilizes the equilibrium. We investigate the closed-loop stiffness variation. Finally, we simulate an underactuated compliant arm to verify the suggested technique.