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Neural control for constrained human-robot interaction with human motion intention estimation and impedance learning

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posted on 2023-06-09, 11:52 authored by Xinbo Yu, Wei He, Yanan LiYanan Li, Chenguang Yang, Changyin Sun
In this paper, an impedance control strategy is proposed for a rigid robot collaborating with human by considering impedance learning and human motion intention estimation. The least square method is used in human impedance identification, and the robot can adjust its impedance parameters according to human impedance model for guaranteeing compliant collaboration. Neural networks (NNs) are employed in human motion intention estimation, so that the robot follows the human actively and human partner costs less control effort. On the other hand, the full-state constraints are considered for operational safety in human-robot interactive processes. Neural control is presented in the control strategy to deal with the dynamic uncertainties and improve the system robustness. Simulation results are carried out to show the effectiveness of the proposed control design.

History

Publication status

  • Published

File Version

  • Accepted version

Page range

2682-2687

Presentation Type

  • paper

Event name

Chinese Automation Congress (CAC), 2017

Event location

Jinan, China

Event type

conference

Event date

20-22 October 2017

Book title

2017 Chinese Automation Congress (CAC)

Department affiliated with

  • Engineering and Design Publications

Notes

© 2018 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

2018-02-01

First Open Access (FOA) Date

2018-02-01

First Compliant Deposit (FCD) Date

2018-02-01

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