Time-/Event-Triggered Adaptive Neural Asymptotic Tracking Control for Nonlinear Systems With Full-State Constraints and Application to a Single-Link Robot

doi:10.1109/TNNLS.2021.3082994

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

	Time-/Event-Triggered Adaptive Neural Asymptotic Tracking Control for Nonlinear Systems With Full-State Constraints and Application to a Single-Link Robot
	Zhang, Jiaming 1; Niu, Ben 1; Wang, Ding 2,5; Wang, Huanqing 4; Zhao, Ping 1; Zong, Guangdeng 3
刊名	IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
	2021-06-01
页码	11
关键词	Artificial neural networks Nonlinear systems Control systems Adaptive systems Backstepping Neurons Task analysis Asymptotic tracking control barrier functions full-state constraints neural networks (NNs) time- event-triggered control uncertain nonlinear systems
ISSN号	2162-237X
DOI	10.1109/TNNLS.2021.3082994
通讯作者	Niu, Ben(niubenbhu@gmail.com)
英文摘要	This study proposes the time-/event-triggered adaptive neural control strategies for the asymptotic tracking problem of a class of uncertain nonlinear systems with full-state constraints. First, we design a time-triggered strategy. The effect caused by the residuals of the estimation via radial basis function (RBF) neural networks (NNs), and the reasonable upper bounds on the first derivative of the reference signal and the derivative of each virtual control, can be eliminated by designing appropriate adaptive laws and utilizing the basic properties of RBF NNs. Moreover, the construction of the barrier Lyapunov functions (BLFs) in this work ensures the compliance of the full-state constraints and also holds the asymptotic output tracking performance. Then, based on the time-triggered strategy, we further design a relative threshold event-triggered strategy. The proposed event-triggered adaptive neural controller can solve the main control objective of this work, that is: 1) the full-state constraint requirements of the system are not violated and 2) the output signal asymptotically tracks the reference signal. Compared with the traditional method, the event-triggered strategy can improve the utilization of communication channels and resources and has greater practical significance. Finally, an example of single-link robot under the proposed two strategies illustrates the validity of the constructed controllers.
资助项目	National Natural Science Foundation of China[61873151] ; National Natural Science Foundation of China[62073201] ; Shandong Provincial Natural Science Foundation of China[ZR2019MF009] ; Taishan Scholar Project of Shandong Province of China[tsqn20190-9078] ; Major Scientific and Technological Innovation Project of Shandong Province, China[2019JAZZ020812] ; Major Program of Shandong Province Natural Science Foundation, China[ZR2018ZB0419]
WOS关键词	BARRIER LYAPUNOV FUNCTIONS ; DYNAMIC SURFACE CONTROL ; FLEXIBLE-JOINT ROBOT ; DESIGN
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:000732380600001
资助机构	National Natural Science Foundation of China ; Shandong Provincial Natural Science Foundation of China ; Taishan Scholar Project of Shandong Province of China ; Major Scientific and Technological Innovation Project of Shandong Province, China ; Major Program of Shandong Province Natural Science Foundation, China
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/46962]
专题	自动化研究所_复杂系统管理与控制国家重点实验室
通讯作者	Niu, Ben
作者单位	1.Shandong Normal Univ, Sch Informat Sci & Engn, Jinan 250014, Peoples R China 2.Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China 3.Qufu Normal Univ, Sch Engn, Rizhao 276826, Peoples R China 4.Bohai Univ, Sch Math & Phys, Jinzhou 121000, Peoples R China 5.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Zhang, Jiaming,Niu, Ben,Wang, Ding,et al. Time-/Event-Triggered Adaptive Neural Asymptotic Tracking Control for Nonlinear Systems With Full-State Constraints and Application to a Single-Link Robot[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2021:11.
APA	Zhang, Jiaming,Niu, Ben,Wang, Ding,Wang, Huanqing,Zhao, Ping,&Zong, Guangdeng.(2021).Time-/Event-Triggered Adaptive Neural Asymptotic Tracking Control for Nonlinear Systems With Full-State Constraints and Application to a Single-Link Robot.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,11.
MLA	Zhang, Jiaming,et al."Time-/Event-Triggered Adaptive Neural Asymptotic Tracking Control for Nonlinear Systems With Full-State Constraints and Application to a Single-Link Robot".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2021):11.