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

	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, Ding2,3 ; Wang, Huanqing 4; Zhao, Ping 1; Zong, Guangdeng 5
发表期刊	IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
ISSN	2162-237X
	2021-06-01
页码	11
通讯作者	Niu, Ben(niubenbhu@gmail.com)
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.
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
DOI	10.1109/TNNLS.2021.3082994
关键词[WOS]	BARRIER LYAPUNOV FUNCTIONS ; DYNAMIC SURFACE CONTROL ; FLEXIBLE-JOINT ROBOT ; DESIGN
收录类别	SCI
语种	英语
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]
项目资助者	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
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS记录号	WOS:000732380600001
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：24[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/173211/46963
专题	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.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 4.Bohai Univ, Sch Math & Phys, Jinzhou 121000, Peoples R China 5.Qufu Normal Univ, Sch Engn, Rizhao 276826, 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.