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Continuous critic learning for robot control in physical human-robot interaction

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conference contribution
posted on 2023-06-09, 09:25 authored by Chen Wang, Yanan LiYanan Li, Shuzhi Sam Ge, Keng Peng Tee, Tong Heng Lee
In this paper, optimal impedance adaptation is investigated for interaction control in constrained motion. The external environment is modeled as a linear system with parameter matrices completely unknown and continuous critic learning is adopted for interaction control. The desired impedance is obtained which leads to an optimal realization of the trajectory tracking and force regulation. As no particular system information is required in the whole process, the proposed interaction control provides a feasible solution to a large number of applications. The validity of the proposed method is verified through simulation studies.

History

Publication status

  • Published

File Version

  • Accepted version

Journal

2013 13th International Conference on Control, Automation and Systems (ICCAS 2013)

ISSN

2093-7121

Publisher

Institute of Electrical and Electronics Engineers

Page range

833-838

Event name

13th International Conference on Control, Automation and Systems (ICCAS)

Event location

Gwangju, South Korea

Event type

conference

Event date

20-23 October 2013

Book title

2013 13th International Conference on Control, Automation and Systems (ICCAS 2013)

ISBN

9788993215052

Department affiliated with

  • Engineering and Design Publications

Notes

(c) 2013 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

2017-12-15

First Open Access (FOA) Date

2017-12-15

First Compliant Deposit (FCD) Date

2017-12-15

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