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

文献类型：期刊论文

作者Zhang, Jiaming5; Niu, Ben5; Wang, Ding2,6; Wang, Huanqing4; Duan, Peiyong3; Zong, Guangdeng1
刊名IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
出版日期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
DOI10.1109/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.
WOS关键词BARRIER LYAPUNOV FUNCTIONS ; DYNAMIC SURFACE CONTROL ; TRACKING CONTROL ; ASYMPTOTIC STABILITY ; OUTPUT CONSTRAINT ; MIMO SYSTEMS ; DESIGN
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研究方向Computer Science ; Engineering
语种英语
WOS记录号WOS:000732907200001
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
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
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.Qufu Normal Univ, Sch Engn, Rizhao 276826, Peoples R China
2.Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China
3.Yantai Univ, Sch Math & Informat Sci, Yantai 264005, 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.

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来源：自动化研究所

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