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Neural Networks Enhanced Adaptive Admittance Control of Optimized Robot-Environment Interaction

文献类型：期刊论文

作者Yang, Chenguang1; Peng, Guangzhu1; Li, Yanan2; Cui, Rongxin3; Cheng, Long4,5; Li, Zhijun6
刊名IEEE TRANSACTIONS ON CYBERNETICS
出版日期2019-07-01
卷号49期号:7页码:2568-2579
ISSN号2168-2267
关键词Admittance control neural networks (NNs) observer optimal adaptive control robot-environment interaction
DOI10.1109/TCYB.2018.2828654
通讯作者Yang, Chenguang(cyang@ieee.org)
英文摘要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.
WOS关键词IMPEDANCE ; PARAMETERS ; VEHICLE
资助项目National Nature Science Foundation[61473120] ; National Nature Science Foundation[61633016] ; National Nature Science Foundation[61472325] ; Science and Technology Planning Project of Guangzhou[201607010006] ; Fundamental Research Funds for the Central Universities[2017ZD057] ; Research Fund for Young Top-Notch Talent of National Ten Thousand Talent Program ; Beijing Municipal Natural Science Foundation[4162066]
WOS研究方向Automation & Control Systems ; Computer Science
语种英语
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号WOS:000466062500015
资助机构National Nature Science Foundation ; Science and Technology Planning Project of Guangzhou ; Fundamental Research Funds for the Central Universities ; Research Fund for Young Top-Notch Talent of National Ten Thousand Talent Program ; Beijing Municipal Natural Science Foundation
源URL[http://ir.ia.ac.cn/handle/173211/24239]  
专题自动化研究所_复杂系统管理与控制国家重点实验室_先进机器人控制团队
通讯作者Yang, Chenguang
作者单位1.South China Univ Technol, Key Lab Autonomous Syst & Networked Control, Coll Automat Sci & Engn, Guangzhou 510640, Guangdong, Peoples R China
2.Univ Sussex, Dept Engn & Design, Brighton BN1 9RH, E Sussex, England
3.Northwestern Polytech Univ, Sch Marine Sci & Technol, Xian 710072, Shaanxi, Peoples R China
4.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
5.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China
6.Univ Sci & Technol China, Dept Automat, Hefei 230026, Anhui, Peoples R China
推荐引用方式
GB/T 7714		 Yang, Chenguang,Peng, Guangzhu,Li, Yanan,et al. Neural Networks Enhanced Adaptive Admittance Control of Optimized Robot-Environment Interaction[J]. IEEE TRANSACTIONS ON CYBERNETICS,2019,49(7):2568-2579.
APA Yang, Chenguang,Peng, Guangzhu,Li, Yanan,Cui, Rongxin,Cheng, Long,&Li, Zhijun.(2019).Neural Networks Enhanced Adaptive Admittance Control of Optimized Robot-Environment Interaction.IEEE TRANSACTIONS ON CYBERNETICS,49(7),2568-2579.
MLA Yang, Chenguang,et al."Neural Networks Enhanced Adaptive Admittance Control of Optimized Robot-Environment Interaction".IEEE TRANSACTIONS ON CYBERNETICS 49.7(2019):2568-2579.

入库方式： OAI收割

来源：自动化研究所

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