Composite Learning Enhanced Neural Control for Robot Manipulator With Output Error Constraints
文献类型：期刊论文
| 作者 | Huang, Dianye1; Yang, Chenguang1; Pan, Yongping2; Cheng, Long3,4
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| 刊名 | IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
 |
| 出版日期 | 2021 |
| 卷号 | 17期号:1页码:209-218 |
| ISSN号 | 1551-3203 |
| 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 |
| 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. |
| WOS关键词 | ADAPTIVE BACKSTEPPING CONTROL ; NONLINEAR-SYSTEMS ; STATE |
| 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 |
| 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.South China Univ Technol, Coll Automat Sci & Engn, Key Lab Autonomous Syst & Networked Control, Guangzhou 510640, Peoples R China 2.Sun Yat Sen Univ, Sch Data & Comp Sci, Guangzhou 510006, Peoples R China 3.Chinese Acad Sci, Inst Automat, Key Lab Complex Syst & Intelligence Sci, 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 | 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. |
入库方式： OAI收割
来源：自动化研究所
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