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Iterative learning control based on stretch and compression mapping for trajectory tracking in human-robot collaboration

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conference contribution
posted on 2023-06-09, 21:55 authored by Jingkang Xia, Deqing Huang, Yanan LiYanan Li, Junpei Zhong
This paper presents a novel iterative learning control (ILC) scheme based on stretch and compression mapping for a robotic manipulator to learn its human partner’s desired trajectory, which is a typical task in the field of human-robot interaction. The proposed scheme is used to reduce the interaction force between the robot and the human partner in repetitive learning process. Thus, the robot can track the human partner’s repetitive trajectory with a small interaction force, leading to little control effort from the human. As the human is involved in the control loop, there are various uncertainties in the system, including variable iteration period in the task under study. The stretch and compression mapping is applied to this problem. In the simulation, the proposed scheme is implemented in the human-robot interaction scenario. Results confirm the effectiveness of the proposed scheme and also illustrate better performance of the proposed ILC compared with other ILC methods with variable periods.

History

Publication status

  • Published

File Version

  • Accepted version

Journal

2020 Chinese Automation Congress (CAC)

ISSN

2688-092X

Publisher

IEEE

Page range

3905-3910

Event name

Chinese Automation Congress

Event location

Shanghai

Event type

conference

Event date

6th November 2020

ISBN

9781728176888

Department affiliated with

  • Engineering and Design Publications

Notes

© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works

Full text available

  • Yes

Peer reviewed?

  • Yes

Legacy Posted Date

2020-10-19

First Open Access (FOA) Date

2021-02-02

First Compliant Deposit (FCD) Date

2020-10-19

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