Paper Info
Title
Learning impedance control of robots with enhanced transient and steady-state control performances
Abstract
This study proposes a learning impedance controller comprising a proportional feedback control term, a composite-learning-based uncertainty estimation term, and a robot-environment interaction control term. The impedance control problem is converted into a particular reference-trajectory tracking problem based on a generated reference trajectory. The proposed controller ensures the exponential convergence of the auxiliary tracking error and the uncertainty estimation error. The interaction control term improves the transient control performance through suppression/encouragement of the incorrect/correct robot movements. The composite-learning update law enhances the transient and steady-state control performances based on the exponential convergence of the uncertainty estimation error and auxiliary tracking error. Finally, the effectiveness and advantages of the proposed impedance controller are validated by theoretical analysis and simulations on a parallel robot.
Year
DOI
Venue
2020
10.1007/s11432-019-2639-6
SCIENCE CHINA-INFORMATION SCIENCES
Keywords
DocType
Volume
robot,adaptive control,neural network,impedance control,parameter convergence
Journal
63
Issue
ISSN
Citations 
9
1674-733X
0
PageRank 
References 
Authors
0.34
0
5
Name
Order
Citations
PageRank
Tairen Sun11359.17
Long Cheng2149273.97
Liang Peng32612.81
Zeng-Guang Hou42293167.18
Yongping Pan566037.53