Neural network-based model predictive tracking control of an uncertain robotic manipulator with input constraints

doi:10.1016/j.isatra.2020.10.009

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 > 机器人应用与理论组

	Neural network-based model predictive tracking control of an uncertain robotic manipulator with input constraints
	Kang, Erlong 2,3,4; Qiao, Hong 1,3,4; Gao, Jie 2,3,4; Yang, Wenjing 5
刊名	ISA TRANSACTIONS
	2021-03-01
卷号	109 页码:89-101
关键词	Model predictive control Neural network Robotic manipulator Unknown dynamics Online learning estimation Input constraints
ISSN号	0019-0578
DOI	10.1016/j.isatra.2020.10.009
通讯作者	Qiao, Hong(hong.qiao@ia.ac.cn)
英文摘要	This paper proposes a neural network-based model predictive control (MPC) method for robotic manipulators with model uncertainty and input constraints. In the presented NN-based MPC structure, two groups of radial basis function neural networks (RBFNNs) are considered for online model estimation and effective optimization. The first group of RBFNNs is introduced as a predictive model for the robotic system with online learning strategies for handling the system uncertainty and improving the model estimation accuracy. The second one is developed for solving the optimization problem. By taking into account an actor-critic scheme with different weights and the same activation function, adaptive learning strategies are established for balancing between optimal tracking performance and predictive system stability. In addition, aiming at guaranteeing the input constraints, a nonquadratic cost function is adopted for the NN-based MPC. The ultimately uniformly boundedness (UUB) of all variables is verified through the Lyapunov approach. Simulation studies are conducted to explain the effectiveness of the proposed method. (C) 2020 ISA. Published by Elsevier Ltd. All rights reserved.
资助项目	National Key Research and Development Program of China[2017YFB1300200] ; National Key Research and Development Program of China[2017YFB1300203] ; National Natural Science Foundation of China[91648205] ; National Natural Science Foundation of China[61627808] ; National Natural Science Foundation of China[91948303] ; Strategic Priority Research Program of Chinese Academy of Science[XDB32050100] ; development of science and technology of Guangdong province special fund project, China[2016B090910001]
WOS研究方向	Automation & Control Systems ; Engineering ; Instruments & Instrumentation
语种	英语
出版者	ELSEVIER SCIENCE INC
WOS记录号	WOS:000618971000009
资助机构	National Key Research and Development Program of China ; National Natural Science Foundation of China ; Strategic Priority Research Program of Chinese Academy of Science ; development of science and technology of Guangdong province special fund project, China
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/43228]
专题	自动化研究所_复杂系统管理与控制国家重点实验室_机器人应用与理论组
通讯作者	Qiao, Hong
作者单位	1.CAS Ctr Excellence Brain Sci & Intelligence Techn, Shanghai 200031, Peoples R China 2.Beijing Key Lab Res & Applicat Robot Intelligence, Beijing 100190, Peoples R China 3.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China 4.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 5.Natl Univ Def Technol, State Key Lab High Performance Comp, Changsha 410073, Peoples R China
推荐引用方式 GB/T 7714	Kang, Erlong,Qiao, Hong,Gao, Jie,et al. Neural network-based model predictive tracking control of an uncertain robotic manipulator with input constraints[J]. ISA TRANSACTIONS,2021,109:89-101.
APA	Kang, Erlong,Qiao, Hong,Gao, Jie,&Yang, Wenjing.(2021).Neural network-based model predictive tracking control of an uncertain robotic manipulator with input constraints.ISA TRANSACTIONS,109,89-101.
MLA	Kang, Erlong,et al."Neural network-based model predictive tracking control of an uncertain robotic manipulator with input constraints".ISA TRANSACTIONS 109(2021):89-101.