Composite Learning Cartesian Impedance Control Under Uncertain Robot Dynamics

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 le...

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Bibliographic Details
Published inIEEE International Conference on Industrial Informatics (INDIN) pp. 1 - 5
Main Authors Pan, Yongping, Ling, Kaiwei, Shi, Tian, Li, Weibing
Format Conference Proceeding
LanguageEnglish
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
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Summary: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.
ISSN:2378-363X
DOI:10.1109/INDIN58382.2024.10774546