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Neural networks enhanced adaptive admittance control of optimized robot-environment interaction

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posted on 2023-06-09, 12:44 authored by Chenguang Yang, Guangzhu Peng, Yanan LiYanan Li, Rongxin Cui, Long Cheng, Zhijun Li
In this paper, an admittance adaptation method has been developed for robots to interact with unknown environments. The environment to be interacted with is modeled as a linear system. In the presence of the unknown dynamics of environments, an observer in robot joint space is employed to estimate the interaction torque, and admittance control is adopted to regulate the robot behavior at interaction points. An adaptive neural controller using the radial basis function is employed to guarantee trajectory tracking. A cost function that defines the interaction performance of torque regulation and trajectory tracking is minimized by admittance adaptation. To verify the proposed method, simulation studies on a robot manipulator are conducted.

History

Publication status

  • Published

File Version

  • Accepted version

Journal

IEEE Transactions on Cybernetics

ISSN

2168-2267

Publisher

Institute of Electrical and Electronics Engineers

Issue

7

Volume

49

Page range

2568-2579

Department affiliated with

  • Engineering and Design Publications

Full text available

  • Yes

Peer reviewed?

  • Yes

Legacy Posted Date

2018-04-05

First Open Access (FOA) Date

2018-05-23

First Compliant Deposit (FCD) Date

2018-04-05

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