Torque sensorless decentralized neuro-optimal control for modular and reconfigurable robots with uncertain environments

doi:10.1016/j.neucom.2017.12.012

	Torque sensorless decentralized neuro-optimal control for modular and reconfigurable robots with uncertain environments
	Dong, Bo1,2 ; Zhou, Fan 1; Liu, Keping 1; Li, Yuanchun 1
发表期刊	NEUROCOMPUTING
ISSN	0925-2312
	2018-03-22
卷号	282 页码:60-73
通讯作者	Liu, Keping(liukeping@ccut.edu.cn) ; Li, Yuanchun(liyc@ccut.edu.cn)
摘要	A technical challenge of addressing the decentralized optimal control problem for modular and reconfigurable robots (MRRs) during environmental contacts is associated with optimal compensation of the uncertain contact force without using force/torque sensors. In this paper, a decentralized control approach is presented for torque sensorless MRRs in contact with uncertain environment via an adaptive dynamic programming (ADP)-based neuro-optimal compensation strategy. The dynamic model of the MRRs is formulated based on a novel joint torque estimation method, which is deployed for each joint model, and the joint dynamic information is utilized effectively to design the feedback controllers, thus making the decentralized optimal control problem of the environmental contacted MRR systems be formulated as an optimal compensation issue of model uncertainty. By using the ADP method, a local online policy iteration algorithm is employed to solve the Hamilton-Jacobi-Bellman (HJB) equation with a modified cost function, which is approximated by constructing a critic neural network, and then the approximate optimal control policy can be derived. The asymptotic stability of the closed-loop MRR system is proved by using the Lyapunov theory. At last, simulations and experiments are performed to verify the effectiveness of the proposed method. (C) 2017 Elsevier B.V. All rights reserved.
关键词	Modular and reconfigurable robot Decentralized control Adaptive dynamic programming (ADP) Optimal control Neural networks
DOI	10.1016/j.neucom.2017.12.012
关键词[WOS]	NONLINEAR INTERCONNECTED SYSTEMS ; REINFORCEMENT LEARNING CONTROL ; CONTROL DESIGN ; FRICTION COMPENSATION ; POLICY ITERATION ; TRACKING CONTROL ; ROBUST-CONTROL ; POSITION ; JOINT ; MANIPULATORS
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[61374051] ; National Natural Science Foundation of China[61773075] ; National Natural Science Foundation of China[61703055] ; State Key Laboratory of Management and Control for Complex Systems[20150102] ; Scientific Technological Development Plan Project in Jilin Province of China[20160520013JH] ; Scientific Technological Development Plan Project in Jilin Province of China[20160414033GH] ; Scientific Technological Development Plan Project in Jilin Province of China[20150520112JH] ; Science and Technology Project of Jilin Provincial Education Department of China[JJKH20170569KJ] ; National Natural Science Foundation of China[61374051] ; National Natural Science Foundation of China[61773075] ; National Natural Science Foundation of China[61703055] ; State Key Laboratory of Management and Control for Complex Systems[20150102] ; Scientific Technological Development Plan Project in Jilin Province of China[20160520013JH] ; Scientific Technological Development Plan Project in Jilin Province of China[20160414033GH] ; Scientific Technological Development Plan Project in Jilin Province of China[20150520112JH] ; Science and Technology project of Jilin Provincial Education Department of China[JJKH20170569KJ]
项目资助者	National Natural Science Foundation of China ; State Key Laboratory of Management and Control for Complex Systems ; Scientific Technological Development Plan Project in Jilin Province of China ; Science and Technology project of Jilin Provincial Education Department of China
WOS研究方向	Computer Science
WOS类目	Computer Science, Artificial Intelligence
WOS记录号	WOS:000424893200007
出版者	ELSEVIER SCIENCE BV
引用统计	被引频次：20[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/173211/28270
专题	复杂系统管理与控制国家重点实验室
通讯作者	Liu, Keping; Li, Yuanchun
作者单位	1.Changchun Univ Technol, Dept Control Sci & Engn, Changchun 130012, Jilin, Peoples R China 2.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
第一作者单位	中国科学院自动化研究所
推荐引用方式 GB/T 7714	Dong, Bo,Zhou, Fan,Liu, Keping,et al. Torque sensorless decentralized neuro-optimal control for modular and reconfigurable robots with uncertain environments[J]. NEUROCOMPUTING,2018,282:60-73.
APA	Dong, Bo,Zhou, Fan,Liu, Keping,&Li, Yuanchun.(2018).Torque sensorless decentralized neuro-optimal control for modular and reconfigurable robots with uncertain environments.NEUROCOMPUTING,282,60-73.
MLA	Dong, Bo,et al."Torque sensorless decentralized neuro-optimal control for modular and reconfigurable robots with uncertain environments".NEUROCOMPUTING 282(2018):60-73.