Composite Learning Enhanced Neural Control for Robot Manipulator With Output Error Constraints

doi:10.1109/TII.2019.2957768

CORC > 自动化研究所 > 中国科学院自动化研究所 > 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

	Composite Learning Enhanced Neural Control for Robot Manipulator With Output Error Constraints
	Huang, Dianye 2; Yang, Chenguang 2; Pan, Yongping 1; Cheng, Long 3,4
刊名	IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
	2021
卷号	17 期号:1 页码:209-218
关键词	Manipulator dynamics Uncertainty Informatics Service robots Lyapunov methods Barrier Lyapunov function (BLF) composite learning (CL) output error constraints radial basis function neural network robot manipulators
ISSN号	1551-3203
DOI	10.1109/TII.2019.2957768
通讯作者	Yang, Chenguang(cyang@ieee.org)
英文摘要	This article presents a control scheme for robot manipulators with the consideration of output error constraints, unknown dynamics, and bounded disturbances. A modified virtual input variable in the second stage design of the dynamic surface control scheme is proposed, which can enhance the robustness of the controller. Bounded disturbances due to the situations that the base is not well fixed if the robot manipulator is mounted at a mobile platform are considered and suppressed. Besides, the detailed implementation process of the composite learning laws adopted for enhancing the radial basis function neural network is presented. Lyapunov stability analysis verifies that the proposed control scheme ensures the trajectory tracking errors stay within predefined boundaries and parameter estimate errors converge without a stringent condition termed persistent excitation. Experimental results show the superiority of the proposed controller regarding parameter estimation and tracking capabilities.
资助项目	National Natural Science Foundation of China[61861136009] ; National Natural Science Foundation of China[61811530281] ; National Natural Science Foundation of China[61703295] ; National Natural Science Foundation of China[61873268] ; National Natural Science Foundation of China[61633016] ; Research Fund for Young Top-Notch Talent of National Ten Thousand Talent Program ; Beijing Municipal Natural Science Foundation[4162066]
WOS关键词	ADAPTIVE BACKSTEPPING CONTROL ; NONLINEAR-SYSTEMS ; STATE
WOS研究方向	Automation & Control Systems ; Computer Science ; Engineering
语种	英语
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号	WOS:000587719200019
资助机构	National Natural Science Foundation of China ; Research Fund for Young Top-Notch Talent of National Ten Thousand Talent Program ; Beijing Municipal Natural Science Foundation
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/41788]
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.Sun Yat Sen Univ, Sch Data & Comp Sci, Guangzhou 510006, Peoples R China 2.South China Univ Technol, Coll Automat Sci & Engn, Key Lab Autonomous Syst & Networked Control, Guangzhou 510640, Peoples R China 3.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China 4.Chinese Acad Sci, Inst Automat, Key Lab Complex Syst & Intelligence Sci, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Huang, Dianye,Yang, Chenguang,Pan, Yongping,et al. Composite Learning Enhanced Neural Control for Robot Manipulator With Output Error Constraints[J]. IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS,2021,17(1):209-218.
APA	Huang, Dianye,Yang, Chenguang,Pan, Yongping,&Cheng, Long.(2021).Composite Learning Enhanced Neural Control for Robot Manipulator With Output Error Constraints.IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS,17(1),209-218.
MLA	Huang, Dianye,et al."Composite Learning Enhanced Neural Control for Robot Manipulator With Output Error Constraints".IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 17.1(2021):209-218.