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Dynamic Neural Network for Bicriteria Weighted Control of Robot Manipulators

文献类型：期刊论文

作者Liu, Mei1,2; He, Li1,2; Shang, Mingsheng2
刊名IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
出版日期2021-10-07
页码14
关键词Manipulators Robots Indexes Mathematical models Manipulator dynamics Optimization Kinematics Bicriteria weighted (BCW) scheme dynamic neural network (DNN) quadratic programming (QP) problem robustness
ISSN号2162-237X
DOI10.1109/TNNLS.2021.3116321
通讯作者Shang, Mingsheng(msshang@cigit.ac.cn)
英文摘要In recent years, bicriteria optimization schemes for manipulator control have become preferred by researchers, given their satisfactory performance. In this article, a bicriteria weighted (BCW) scheme to remedy joint drift and minimize the infinity norm of joint velocity is proposed. The scheme adopts a novel repetitive motion index that can theoretically decouple the joint error and the position error, which many conventional cyclic motion generation schemes cannot achieve. Subsequently, through transformation, the BCW scheme is converted into a time-varying quadratic programming (QP) problem. Then, a dynamic neural network (DNN) system with a new Fisher-Burmeister function is proposed to address the resulting QP problem. It is proven that the proposed DNN system is free of residual errors, which means that the actual solution is able to converge to the theoretical solution. Another essential feature of the DNN system is that it has a suppression effect on noise. To demonstrate the convergence and robustness of the proposed DNN system, comparative simulations are carried out in nominal and noisy environments. Finally, experiments on Franka Emika Panda are conducted to elucidate the availability of the BCW scheme addressed by the DNN system.
资助项目National Natural Science Foundation of China[62072429] ; National Natural Science Foundation of China[62176109] ; Natural Science Foundation of Chongqing, China[cstc2020jcyj-zdxmX0028] ; Chinese Academy of Sciences Light of West China Program ; Natural Science Foundation of Gansu Province[21JR7RA531] ; Natural Science Foundation of Gansu Province[20JR10RA639] ; Chongqing Key Laboratory of Mobile Communications Technology[cquptmct-202004] ; Fundamental Research Funds for the Central Universities[lzujbky-2021-65]
WOS研究方向Computer Science ; Engineering
语种英语
WOS记录号WOS:000732351500001
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
源URL[http://119.78.100.138/handle/2HOD01W0/14746]  
专题中国科学院重庆绿色智能技术研究院
通讯作者Shang, Mingsheng
作者单位1.Lanzhou Univ, Sch Informat Sci & Engn, Lanzhou 730000, Peoples R China
2.Chinese Acad Sci, Chongqing Key Lab Big Data & Intelligent Comp, Chongqing Inst Green & Intelligent Technol, Chongqing 400714, Peoples R China
推荐引用方式
GB/T 7714		 Liu, Mei,He, Li,Shang, Mingsheng. Dynamic Neural Network for Bicriteria Weighted Control of Robot Manipulators[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2021:14.
APA Liu, Mei,He, Li,&Shang, Mingsheng.(2021).Dynamic Neural Network for Bicriteria Weighted Control of Robot Manipulators.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,14.
MLA Liu, Mei,et al."Dynamic Neural Network for Bicriteria Weighted Control of Robot Manipulators".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2021):14.

入库方式： OAI收割

来源：重庆绿色智能技术研究院

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