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

doi:10.1109/TNNLS.2020.2963998

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

	RNN for Perturbed Manipulability Optimization of Manipulators Based on a Distributed Scheme: A Game-Theoretic Perspective
	Zhang, Jiazheng 1,3; Jin, Long 1,3; Cheng, Long 1,2
刊名	IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
	2020-12-01
卷号	31 期号:12 页码:5116-5126
关键词	Optimization Neural networks Task analysis Nash equilibrium Manipulator dynamics Distributed control game theory manipulability optimization neural network redundancy resolution
ISSN号	2162-237X
DOI	10.1109/TNNLS.2020.2963998
通讯作者	Jin, Long(longjin@ieee.org)
英文摘要	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.
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]
WOS关键词	ZHANG NEURAL-NETWORK ; REDUNDANT MANIPULATORS
WOS研究方向	Computer Science ; Engineering
语种	英语
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号	WOS:000595533300007
资助机构	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
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/42740]
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	Jin, Long
作者单位	1.Chinese Acad Sci, State Key Lab Management & Control Complex Syst, Inst Automat, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China 3.Lanzhou Univ, Sch Informat Sci & Engn, Lanzhou 730000, Peoples R China
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.