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

Yu, Xinbo, He, Wei, Li, Yanan, Yang, Chenguang and Sun, Changyin (2018) Neural control for constrained human-robot interaction with human motion intention estimation and impedance learning. In: Chinese Automation Congress (CAC), 2017, 20-22 October 2017, Jinan, China.

[img] PDF - Accepted Version
Download (120kB)

Abstract

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.

Item Type: Conference or Workshop Item (Paper)
Additional Information: © 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.
Schools and Departments: School of Engineering and Informatics > Engineering and Design
Depositing User: Yanan Li
Date Deposited: 01 Feb 2018 11:46
Last Modified: 01 Feb 2018 11:46
URI: http://sro.sussex.ac.uk/id/eprint/73278

View download statistics for this item

📧 Request an update