Composite Learning Cartesian Impedance Control Under Uncertain Robot Dynamics

• Published in: IEEE International Conference on Industrial Informatics (INDIN) pp. 1 - 5
• Main Authors: Pan, Yongping, Ling, Kaiwei, Shi, Tian, Li, Weibing
• Format: Conference Proceeding
• Language: English
• Published: IEEE 18.08.2024
• Subjects: Accuracy; Adaptive control; Collaborative robots; Dynamics; End effectors; Impedance; impedance control; Informatics; Parameter estimation; parametric uncertainty; robot dynamics; robot inter-active control; Safety; Stability; Trajectory tracking
• DOI: 10.1109/INDIN58382.2024.10774546

Cartesian impedance control plays a significant role in improving the safety and compliance of robot end-effectors when executing collaborative tasks with humans or environments. However, achieving target impedance is challenging under uncertain robot dynamics. In this study, we raise a composite learning-based Cartesian impedance control method to ensure exact Cartesian trajectory tracking in free motion and Cartesian target impedance in interaction under uncertain robot dynamics. Introducing a composite learning update to precise robot modeling online, so the exponential convergence and passivity of the closed-loop robot dynamics are guaranteed under a weak condition known as interval excitation. The efficacy and superiority of this method have been demonstrated through experiments on a collaborative robot with 7 degrees of freedom known as Franka Emika Panda.