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Neural networks enhanced adaptive admittance control of optimized robot-environment interaction
journal contribution
posted on 2023-06-09, 12:44 authored by Chenguang Yang, Guangzhu Peng, Yanan LiYanan Li, Rongxin Cui, Long Cheng, Zhijun LiIn 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.
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Publication status
- Published
File Version
- Accepted version
Journal
IEEE Transactions on CyberneticsISSN
2168-2267Publisher
Institute of Electrical and Electronics EngineersExternal DOI
Issue
7Volume
49Page range
2568-2579Department affiliated with
- Engineering and Design Publications
Full text available
- Yes
Peer reviewed?
- Yes
Legacy Posted Date
2018-04-05First Open Access (FOA) Date
2018-05-23First Compliant Deposit (FCD) Date
2018-04-05Usage metrics
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