Adaptive Neural Control of Nonlinear Nonstrict Feedback Systems With Full-State Constraints: A Novel Nonlinear Mapping Method

doi:10.1109/TNNLS.2021.3104877

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

	Adaptive Neural Control of Nonlinear Nonstrict Feedback Systems With Full-State Constraints: A Novel Nonlinear Mapping Method
	Zhang, Jiaming 5; Niu, Ben 5; Wang, Ding 3,6; Wang, Huanqing 4; Duan, Peiyong 1; Zong, Guangdeng 2
刊名	IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
	2021-08-20
页码	9
关键词	Nonlinear systems Artificial neural networks Design methodology Adaptive control Time-varying systems Fuzzy logic Backstepping Asymptotic tracking control neural networks (NN) nonlinear mapping (NM) nonstrict feedback structure time-varying full-state constraints uncertain nonlinear system
ISSN号	2162-237X
DOI	10.1109/TNNLS.2021.3104877
通讯作者	Niu, Ben(niubenbhu@gmail.com)
英文摘要	In this work, a neural-networks (NNs)-based adaptive asymptotic tracking control scheme is presented for a class of uncertain nonstrict feedback nonlinear systems with time-varying full-state constraints. First, we construct a novel exponentially decaying nonlinear mapping to map the constrained system states to new system states without constraints. Instead of the traditional barrier Lyapunov function methods, the feasible conditions which require the virtual control signals satisfying the constraint requirements are removed. By employing the Nussbaum design method to eliminate the effect of unknown control gains, the general assumption about the signs of the unknown control gains is relaxed. Then, the nonstrict feedback form of the system can be pulled back to the strict feedback form through the basic properties of radial basis function NNs. Simultaneously, the intermediate control signals and the desired controller are constructed by the backstepping process and the Nussbaum design method. The designed controller can ensure that all signals in the whole closed-loop system are bounded without the violation of the constraints and hold the asymptotic tracking performance. In the end, a practical example about a brush dc motor driving a one-link robot manipulator is given to illustrate the effectiveness of the proposed design scheme.
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 ; TRACKING CONTROL ; ASYMPTOTIC STABILITY ; OUTPUT CONSTRAINT ; MIMO SYSTEMS ; 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:000732907200001
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/173211/47025]
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.Yantai Univ, Sch Math & Informat Sci, Yantai 264005, Peoples R China 2.Qufu Normal Univ, Sch Engn, Rizhao 276826, Peoples R China 3.Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China 4.Bohai Univ, Sch Math & Phys, Jinzhou 121000, Peoples R China 5.Shandong Normal Univ, Sch Informat Sci & Engn, Jinan 250014, Peoples R China 6.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. Adaptive Neural Control of Nonlinear Nonstrict Feedback Systems With Full-State Constraints: A Novel Nonlinear Mapping Method[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2021:9.
APA	Zhang, Jiaming,Niu, Ben,Wang, Ding,Wang, Huanqing,Duan, Peiyong,&Zong, Guangdeng.(2021).Adaptive Neural Control of Nonlinear Nonstrict Feedback Systems With Full-State Constraints: A Novel Nonlinear Mapping Method.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,9.
MLA	Zhang, Jiaming,et al."Adaptive Neural Control of Nonlinear Nonstrict Feedback Systems With Full-State Constraints: A Novel Nonlinear Mapping Method".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2021):9.