Tracking of Uncertain Robotic Manipulators Using Event-Triggered Model Predictive Control With Learning Terminal Cost

doi:10.1109/TASE.2022.3152166

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

	Tracking of Uncertain Robotic Manipulators Using Event-Triggered Model Predictive Control With Learning Terminal Cost
	Kang, Erlong 3,4,5; Qiao, Hong 1,2,4; Chen, Ziyu 3,4; Gao, Jie 3,4,5
刊名	IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING
	2022-03-03
页码	15
关键词	Robots Costs Predictive models Optimization Stability analysis Manipulator dynamics Predictive control Model predictive control robotic manipulator leaning terminal cost neural networks event-triggered mechanism unknown dynamics
ISSN号	1545-5955
DOI	10.1109/TASE.2022.3152166
通讯作者	Qiao, Hong(hong.qiao@ia.ac.cn)
英文摘要	This paper presents an event-triggered model predictive control (MPC) strategy with learning terminal cost for robotic manipulators containing model uncertainty and input constraints. In the proposed MPC structure, an adaptive predictive model for the robotic system is established by radial basis function neural networks (RBFNNs) firstly. Then, a terminal cost adjusted by the global learning mechanism is constructed. Both global steady-state optimization and transient fast convergence are achieved by adding the learning terminal cost to the MPC scheme. After that, a triggering condition of the MPC solving is developed based on the predictive model's weights and the predictive tracking error. Besides, the condition to avoid Zeno behavior is obtained. The recursive feasibility of the proposed MPC strategy is verified, and the ultimately uniformly boundedness (UUB) of all variables is proved according to the Lyapunov theorem. Finally, experiments based on an xMate7 Pro robot are conducted to demonstrate the effectiveness of the presented method.
资助项目	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[2016B090910001]
WOS关键词	ROBUST TRAJECTORY TRACKING ; DYNAMICS ; MPC
WOS研究方向	Automation & Control Systems
语种	英语
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号	WOS:000767816400001
资助机构	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
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/48053]
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.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 2.CAS Ctr Excellence Brain Sci & Intelligence Techn, Shanghai 200031, Peoples R China 3.Beijing Key Lab Res & Applicat Robot Intelligence, Beijing 100190, Peoples R China 4.Chinese Acad Sci, State Key Lab Management & Control Complex Syst, Inst Automat, Beijing 100190, Peoples R China 5.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China
推荐引用方式 GB/T 7714	Kang, Erlong,Qiao, Hong,Chen, Ziyu,et al. Tracking of Uncertain Robotic Manipulators Using Event-Triggered Model Predictive Control With Learning Terminal Cost[J]. IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING,2022:15.
APA	Kang, Erlong,Qiao, Hong,Chen, Ziyu,&Gao, Jie.(2022).Tracking of Uncertain Robotic Manipulators Using Event-Triggered Model Predictive Control With Learning Terminal Cost.IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING,15.
MLA	Kang, Erlong,et al."Tracking of Uncertain Robotic Manipulators Using Event-Triggered Model Predictive Control With Learning Terminal Cost".IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING (2022):15.