Iterative Reference Learning for Cartesian Impedance Control of Robot Manipulators

Abstract

In this paper, an iterative learning strategy was developed to improve trajectory tracking for an impedance-controlled robot manipulator. In this learning strategy, an update law was proposed to modify the Cartesian reference of an impedance controller. Also, the conditions that ensure its convergence considering the dynamics of the robot were derived. Finally, an experimental evaluation was performed using a Franka Emika Panda robot in two different robot tasks, and its results showed that robot task completion was achieved in a lower number of iterations, while maintaining a smooth physical interaction between the robot and its surroundings.