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Neural networks enhancedadaptive admittance control of optimized robot-environment interaction
Chenguang Yang; Guangzhu Peng; Yanan Li; Rongxin Cui; Long Cheng; Zhijun Li
Source PublicationIEEE Transactions on Cybernetics
2018
AbstractIn 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.
KeywordAdmittance control neural networks (NNs) observer optimal adaptive control robot–environment interaction
DOI10.1109/TCYB.2018.2828654
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/23168
Collection复杂系统管理与控制国家重点实验室_先进机器人
Recommended Citation
GB/T 7714
Chenguang Yang,Guangzhu Peng,Yanan Li,et al. Neural networks enhancedadaptive admittance control of optimized robot-environment interaction[J]. IEEE Transactions on Cybernetics,2018.
APA Chenguang Yang,Guangzhu Peng,Yanan Li,Rongxin Cui,Long Cheng,&Zhijun Li.(2018).Neural networks enhancedadaptive admittance control of optimized robot-environment interaction.IEEE Transactions on Cybernetics.
MLA Chenguang Yang,et al."Neural networks enhancedadaptive admittance control of optimized robot-environment interaction".IEEE Transactions on Cybernetics (2018).
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