Composite Learning Enhanced Robot Impedance Control

doi:10.1109/TNNLS.2019.2912212

	Composite Learning Enhanced Robot Impedance Control
	Sun, Tairen 1; Peng, Liang1 ; Cheng, Long1,2 ; Hou, Zeng-Guang1,2,3 ; Pan, Yongping 4
发表期刊	IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
ISSN	2162-237X
	2020-03-01
卷号	31 期号:3 页码:1052-1059
通讯作者	Hou, Zeng-Guang(zengguang.hou@ia.ac.cn)
摘要	The desired impedance dynamics can be achieved for a robot if and only if an impedance error converges to zero or a small neighborhood of zero. Although the convergence of impedance errors is important, it is seldom obtained in the existing impedance controllers due to robots modeling uncertainties and external disturbances. This brief proposes two composite learning impedance controllers (CLICs) for robots with parameter uncertainties based on whether a factorization assumption is satisfied or not. In the proposed control designs, the convergence of impedance errors, reflected by the convergence of parameter estimation errors and some auxiliary errors, is achieved by using composite learning laws under a relaxed excitation condition. The theoretical results are proven based on the Lyapunov theory. The effectiveness and advantages of the proposed CLICs are validated by simulations on a parallel robot in three cases.
关键词	Impedance Convergence Robots Stability criteria Uncertainty Parameter estimation Adaptive control composite adaptation impedance control learning control parameter convergence robot
DOI	10.1109/TNNLS.2019.2912212
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[61720106012] ; National Natural Science Foundation of China[61533016] ; National Natural Science Foundation of China[61873268] ; National Natural Science Foundation of China[61633016] ; National Natural Science Foundation of China[61703295] ; National Natural Science Foundation of China[61603386] ; Beijing Natural Science Foundation[3171001] ; Beijing Natural Science Foundation[L172050] ; Strategic Priority Research Program of Chinese Academy of Science[XDB32040000] ; Beijing Municipal Natural Science Foundation[L182060]
项目资助者	National Natural Science Foundation of China ; Beijing Natural Science Foundation ; Strategic Priority Research Program of Chinese Academy of Science ; Beijing Municipal Natural Science Foundation
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS记录号	WOS:000521961300029
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
七大方向——子方向分类	智能控制
引用统计	被引频次：54[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/173211/38765
专题	复杂系统认知与决策实验室_先进机器人
通讯作者	Hou, Zeng-Guang
作者单位	1.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China 3.CAS Ctr Excellence Brain Sci & Intelligence Techn, Beijing 100190, Peoples R China 4.Sun Yat Sen Univ, Sch Data & Comp Sci, Guangzhou 510006, Peoples R China
第一作者单位	中国科学院自动化研究所
通讯作者单位	中国科学院自动化研究所
推荐引用方式 GB/T 7714	Sun, Tairen,Peng, Liang,Cheng, Long,et al. Composite Learning Enhanced Robot Impedance Control[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2020,31(3):1052-1059.
APA	Sun, Tairen,Peng, Liang,Cheng, Long,Hou, Zeng-Guang,&Pan, Yongping.(2020).Composite Learning Enhanced Robot Impedance Control.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,31(3),1052-1059.
MLA	Sun, Tairen,et al."Composite Learning Enhanced Robot Impedance Control".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 31.3(2020):1052-1059.