RNN for Perturbed Manipulability Optimization of Manipulators Based on a Distributed Scheme: A Game-Theoretic Perspective

doi:10.1109/TNNLS.2020.2963998

	RNN for Perturbed Manipulability Optimization of Manipulators Based on a Distributed Scheme: A Game-Theoretic Perspective
	Zhang, Jiazheng 1,2; Jin, Long 1,2; Cheng, Long2,3
发表期刊	IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
ISSN	2162-237X
	2020-12-01
卷号	31 期号:12 页码:5116-5126
通讯作者	Jin, Long(longjin@ieee.org)
摘要	In order to leverage the unique advantages of redundant manipulators, avoiding the singularity during motion planning and control should be considered as a fundamental issue to handle. In this article, a distributed scheme is proposed to improve the manipulability of redundant manipulators in a group. To this end, the manipulability index is incorporated into the cooperative control of multiple manipulators in a distributed network, which is used to guide manipulators to adjust to the optimal spatial position. Moreover, from the perspective of game theory, this article formulates the problem into a Nash equilibrium. Then, a neural network with anti-noise ability is constructed to seek and approximate the optimal strategy profile of the Nash equilibrium problem with time-varying parameters. Theoretical analyses show that the neural network model has the superior global convergence and noise immunity. Finally, simulation results demonstrate that the neural network is effective in real-time cooperative motion generation of multiple redundant manipulators under perturbations in distributed networks.
关键词	Optimization Neural networks Task analysis Nash equilibrium Manipulator dynamics Distributed control game theory manipulability optimization neural network redundancy resolution
DOI	10.1109/TNNLS.2020.2963998
关键词[WOS]	ZHANG NEURAL-NETWORK ; REDUNDANT MANIPULATORS
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[61703189] ; National Natural Science Foundation of China[U1913209] ; National Natural Science Foundation of China[61873268] ; National Natural Science Foundation of China[61633016] ; National Key Research and Development Program of China[2017YFE0118900] ; Natural Science Foundation of Gansu Province, China[18JR3RA264] ; Sichuan Science and Technology Program[19YYJC1656] ; State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences[20190112] ; Fundamental Research Funds for the Central Universities[lzujbky-2019-89] ; Beijing Municipal Natural Science Foundation[JQ19020] ; Beijing Municipal Natural Science Foundation[L182060]
项目资助者	National Natural Science Foundation of China ; National Key Research and Development Program of China ; Natural Science Foundation of Gansu Province, China ; Sichuan Science and Technology Program ; State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences ; Fundamental Research Funds for the Central Universities ; 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:000595533300007
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
七大方向——子方向分类	机器人感知与决策
引用统计	被引频次：55[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/173211/42741
专题	复杂系统认知与决策实验室_先进机器人
通讯作者	Jin, Long
作者单位	1.Lanzhou Univ, Sch Informat Sci & Engn, Lanzhou 730000, Peoples R China 2.Chinese Acad Sci, State Key Lab Management & Control Complex Syst, Inst Automat, Beijing 100190, Peoples R China 3.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, 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	Zhang, Jiazheng,Jin, Long,Cheng, Long. RNN for Perturbed Manipulability Optimization of Manipulators Based on a Distributed Scheme: A Game-Theoretic Perspective[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2020,31(12):5116-5126.
APA	Zhang, Jiazheng,Jin, Long,&Cheng, Long.(2020).RNN for Perturbed Manipulability Optimization of Manipulators Based on a Distributed Scheme: A Game-Theoretic Perspective.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,31(12),5116-5126.
MLA	Zhang, Jiazheng,et al."RNN for Perturbed Manipulability Optimization of Manipulators Based on a Distributed Scheme: A Game-Theoretic Perspective".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 31.12(2020):5116-5126.