CAC 2017.pdf (117.3 kB)
Neural control for constrained human-robot interaction with human motion intention estimation and impedance learning
presentation
posted on 2023-06-09, 11:52 authored by Xinbo Yu, Wei He, Yanan LiYanan Li, Chenguang Yang, Changyin SunIn 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
External DOI
Page range
2682-2687Presentation Type
- paper
Event name
Chinese Automation Congress (CAC), 2017Event location
Jinan, ChinaEvent type
conferenceEvent date
20-22 October 2017Book 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
2018-02-01First Open Access (FOA) Date
2018-02-01First Compliant Deposit (FCD) Date
2018-02-01Usage metrics
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