Iterative Learning Algorithm Design for Variable Admittance Control Tuning of A Robotic Lift Assistant System

Event
WCX™ 17: SAE World Congress Experience
Authors Abstract
Content
The human-robot interaction (HRI) is involved in a lift assistant system of manufacturing assembly line. The admittance model is applied to control the end effector motion by sensing intention from force of applied by a human operator. The variable admittance including virtual damping and virtual mass can improve the performance of the systems. But the tuning process of variable admittance is un-convenient and challenging part during the real test for designers, while the offline simulation is lack of learning process and interaction with human operator. In this paper, the Iterative learning algorithm is proposed to emulate the human learning process and facilitate the variable admittance control design. The relationship between manipulate force and object moving speed is demonstrated from simulation data. The effectiveness of the approach is verified by comparing the simulation results between two admittance control strategies.
Meta TagsDetails
DOI
https://doi.org/10.4271/2017-01-0288
Pages
6
Citation
Wu, H., and Li, M., "Iterative Learning Algorithm Design for Variable Admittance Control Tuning of A Robotic Lift Assistant System," SAE Int. J. Engines 10(2):203-208, 2017, https://doi.org/10.4271/2017-01-0288.
Additional Details
Publisher
Published
Mar 28, 2017
Product Code
2017-01-0288
Content Type
Journal Article
Language
English