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

Yang, Chenguang, Peng, Guangzhu, Li, Yanan, Cui, Rongxin, Cheng, Long and Li, Zhijun (2018) Neural networks enhanced adaptive admittance control of optimized robot-environment interaction. IEEE Transactions on Cybernetics. ISSN 2168-2267

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Abstract

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.

Item Type: Article
Schools and Departments: School of Engineering and Informatics > Engineering and Design
Depositing User: Yanan Li
Date Deposited: 05 Apr 2018 13:41
Last Modified: 15 Aug 2018 14:57
URI: http://sro.sussex.ac.uk/id/eprint/74843

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