Adaptive Neural Output-Feedback Controller Design of Switched Nonlower Triangular Nonlinear Systems With Time Delays

doi:10.1109/TNNLS.2019.2952108

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 > 智能化团队

	Adaptive Neural Output-Feedback Controller Design of Switched Nonlower Triangular Nonlinear Systems With Time Delays
	Niu, Ben 3; Wang, Ding 4,5; Liu, Ming 1; Song, Xinmin 3; Wang, Huanqing 2; Duan, Peiyong 3
刊名	IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
	2020-10-01
卷号	31 期号:10 页码:4084-4093
关键词	Switches Adaptive systems Nonlinear systems Delay effects Backstepping Stability analysis Adaptive control average dwell time (ADT) neural networks (NNs) nonlower triangular form switched nonlinear systems time delays
ISSN号	2162-237X
DOI	10.1109/TNNLS.2019.2952108
通讯作者	Niu, Ben(niubenbhu@gmail.com)
英文摘要	In this article, we study the issue of adaptive neural output-feedback controller design for a class of uncertain switched time-delay nonlinear systems with nonlower triangular structure. The prominent contribution of this article is that the delay-dependent stability criterion of nonswitched nonlinear systems is successfully extended to that of switched nonlower triangular nonlinear systems. The design algorithm is listed as follows. First, a switched state observer is designed such that the error dynamic system can be generated. Second, neural networks, adaptive backstepping technique, and variable separation method are, respectively, applied to construct a common controller for all subsystems, in which the Lyapunov-Krasovskii functionals are deliberately constructed such that the average dwell-time scheme can be employed to guarantee the stability and performance of the closed-loop system, despite the existence of time delays. Third, the stability analysis process confirms in detail that all the variables of the closed-loop system are semiglobally uniformly ultimately bounded. Finally, simulation study is given to show the validity of the proposed control approach.
资助项目	National Natural Science Foundation of China[61873151] ; National Natural Science Foundation of China[61873152] ; National Natural Science Foundation of China[61773373] ; National Natural Science Foundation of China[61773192] ; National Natural Science Foundation of China[61773246] ; Shandong Provincial Natural Science Foundation, China[ZR2019MF009] ; Taishan Scholar Project of Shandong Province of China[tsqn201909078] ; Major Program of Shandong Province Natural Science Foundation[ZR2018ZB0419]
WOS关键词	H-INFINITY CONTROL ; FUZZY CONTROL ; STABILIZATION ; STABILITY ; TRACKING
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:000576436600025
资助机构	National Natural Science Foundation of China ; Shandong Provincial Natural Science Foundation, China ; Taishan Scholar Project of Shandong Province of China ; Major Program of Shandong Province Natural Science Foundation
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/42090]
专题	自动化研究所_复杂系统管理与控制国家重点实验室_智能化团队
通讯作者	Niu, Ben
作者单位	1.Harbin Inst Technol, Sch Astronaut, Harbin 150001, Peoples R China 2.Bohai Univ, Coll Math & Phys, Jinzhou 121013, Peoples R China 3.Shandong Normal Univ, Sch Informat Sci & Engn, Jinan 250014, Peoples R China 4.Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China 5.Chinese Acad Sci, State Key Lab Management & Control Complex Syst, Inst Automat, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Niu, Ben,Wang, Ding,Liu, Ming,et al. Adaptive Neural Output-Feedback Controller Design of Switched Nonlower Triangular Nonlinear Systems With Time Delays[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2020,31(10):4084-4093.
APA	Niu, Ben,Wang, Ding,Liu, Ming,Song, Xinmin,Wang, Huanqing,&Duan, Peiyong.(2020).Adaptive Neural Output-Feedback Controller Design of Switched Nonlower Triangular Nonlinear Systems With Time Delays.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,31(10),4084-4093.
MLA	Niu, Ben,et al."Adaptive Neural Output-Feedback Controller Design of Switched Nonlower Triangular Nonlinear Systems With Time Delays".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 31.10(2020):4084-4093.