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Composite Learning Enhanced Robot Impedance Control

文献类型：期刊论文

作者Sun, Tairen1; Peng, Liang1; Cheng, Long1,4; Hou, Zeng-Guang1,3,4; Pan, Yongping2
刊名IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
出版日期2020-03-01
卷号31期号:3页码:1052-1059
关键词Impedance Convergence Robots Stability criteria Uncertainty Parameter estimation Adaptive control composite adaptation impedance control learning control parameter convergence robot
ISSN号2162-237X
DOI10.1109/TNNLS.2019.2912212
通讯作者Hou, Zeng-Guang(zengguang.hou@ia.ac.cn)
英文摘要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.
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]
WOS研究方向Computer Science ; Engineering
语种英语
WOS记录号WOS:000521961300029
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
资助机构National Natural Science Foundation of China ; Beijing Natural Science Foundation ; Strategic Priority Research Program of Chinese Academy of Science ; Beijing Municipal Natural Science Foundation
源URL[http://ir.ia.ac.cn/handle/173211/38764]  
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.Sun Yat Sen Univ, Sch Data & Comp Sci, Guangzhou 510006, Peoples R China
3.CAS Ctr Excellence Brain Sci & Intelligence Techn, Beijing 100190, Peoples R China
4.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China
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.

入库方式： OAI收割

来源：自动化研究所

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