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Iterative learning-based robotic controller with prescribed human-robot interaction force

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posted on 2023-06-10, 01:20 authored by XUEYAN XING, Kamran Maqsood, Deqing Huang, Chenguang Yang, Yanan LiYanan Li
In this article, an iterative-learning-based robotic controller is developed, which aims at providing a prescribed assistance or resistance force to the human user. In the proposed controller, the characteristic parameter of the human upper limb movement is first learned by the robot using the measurable interaction force, a recursive least square (RLS)-based estimator, and the Adam optimization method. Then, the desired trajectory of the robot can be obtained, tracking which the robot can supply the human's upper limb with a prescribed interaction force. Using this controller, the robot automatically adjusts its reference trajectory to embrace the differences between different human users with diverse degrees of upper limb movement characteristics. By designing a performance index in the form of interaction force integral, potential adverse effects caused by the time-related uncertainty during the learning process can be addressed. The experimental results demonstrate the effectiveness of the proposed method in supplying the prescribed interaction force to the human user.

Funding

The Game Theory of Human-Robot Interaction - HRIgame; G2929; EPSRC-ENGINEERING & PHYSICAL SCIENCES RESEARCH COUNCIL

History

Publication status

  • Published

File Version

  • Accepted version

Journal

IEEE Transactions on Automation Science and Engineering

ISSN

1545-5955

Publisher

Institute of Electrical and Electronics Engineers

Page range

1-14

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

2021-10-07

First Open Access (FOA) Date

2021-10-07

First Compliant Deposit (FCD) Date

2021-10-06

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