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

doi:10.1109/TCYB.2018.2828654

	Neural Networks Enhanced Adaptive Admittance Control of Optimized Robot-Environment Interaction
	Yang, Chenguang 1; Peng, Guangzhu 1; Li, Yanan 2; Cui, Rongxin 3; Cheng, Long4,5 ; Li, Zhijun 6
发表期刊	IEEE TRANSACTIONS ON CYBERNETICS
ISSN	2168-2267
	2019-07-01
卷号	49 期号:7 页码:2568-2579
通讯作者	Yang, Chenguang(cyang@ieee.org)
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.
关键词	Admittance control neural networks (NNs) observer optimal adaptive control robot-environment interaction
DOI	10.1109/TCYB.2018.2828654
关键词[WOS]	IMPEDANCE ; PARAMETERS ; VEHICLE
收录类别	SCI
语种	英语
资助项目	Beijing Municipal Natural Science Foundation[4162066] ; Research Fund for Young Top-Notch Talent of National Ten Thousand Talent Program ; Fundamental Research Funds for the Central Universities[2017ZD057] ; Science and Technology Planning Project of Guangzhou[201607010006] ; National Nature Science Foundation[61472325] ; National Nature Science Foundation[61633016] ; National Nature Science Foundation[61473120] ; 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]
项目资助者	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
WOS研究方向	Automation & Control Systems ; Computer Science
WOS类目	Automation & Control Systems ; Computer Science, Artificial Intelligence ; Computer Science, Cybernetics
WOS记录号	WOS:000466062500015
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：140[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/24240
专题	复杂系统认知与决策实验室_先进机器人
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.