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Time-/Event-Triggered Adaptive Neural Asymptotic Tracking Control of Nonlinear Interconnected Systems With Unmodeled Dynamics and Prescribed Performance

文献类型：期刊论文

作者Cheng, Ting-Ting1; Niu, Ben1; Zhang, Jia-Ming1; Wang, Ding2,3; Wang, Zhen-Hua1
刊名IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
出版日期2021-12-06
页码11
关键词Interconnected systems Artificial neural networks Closed loop systems Multi-agent systems Asymptotic stability Switches Stability criteria Asymptotic tracking control event-triggered control neural networks (NNs) nonlinear interconnected systems prescribed performance control (PPC) unmodeled dynamics
ISSN号2162-237X
DOI10.1109/TNNLS.2021.3129228
通讯作者Niu, Ben(niubensdnu@163.com) ; Wang, Zhen-Hua(wzhua111@126.com)
英文摘要This article proposes two adaptive asymptotic tracking control schemes for a class of interconnected systems with unmodeled dynamics and prescribed performance. By applying an inherent property of radial basis function (RBF) neural networks (NNs), the design difficulties aroused from the unknown interactions among subsystems and unmodeled dynamics are overcome. Then, in order to ensure that the tracking errors can be suppressed in the specified range, the constrained control problem is transformed into the stabilization problem by using an auxiliary function. Based on the adaptive backstepping method, a time-triggered controller is constructed. It is proven that under the framework of Barbalat's lemma, all the variables in the closed-loop system are bounded and the tracking errors are further ensured to converge to zero asymptotically. Furthermore, the event-triggered strategy with a variable threshold is adopted to make more precise control such that the better system performance can be obtained, which reduces the system communication burden under the condition of limited communication resources. Finally, an illustrative example is provided to demonstrate the effectiveness of the proposed control scheme.
WOS关键词OUTPUT-FEEDBACK CONTROL ; FAULT-TOLERANT CONTROL ; DESIGN
资助项目National Natural Science Foundation of China[61873151] ; National Natural Science Foundation of China[61803237] ; National Natural Science Foundation of China[62073201] ; Shandong Provincial Natural Science Foundation of China[ZR2019MF009] ; Taishan Scholar Project of Shandong Province of China[tsqn201909078] ; 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:000732080600001
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/173211/46818]  
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; Wang, Zhen-Hua
作者单位1.Shandong Normal Univ, Sch Informat Sci & Engn, Jinan 250014, Shandong, 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
推荐引用方式
GB/T 7714		 Cheng, Ting-Ting,Niu, Ben,Zhang, Jia-Ming,et al. Time-/Event-Triggered Adaptive Neural Asymptotic Tracking Control of Nonlinear Interconnected Systems With Unmodeled Dynamics and Prescribed Performance[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2021:11.
APA Cheng, Ting-Ting,Niu, Ben,Zhang, Jia-Ming,Wang, Ding,&Wang, Zhen-Hua.(2021).Time-/Event-Triggered Adaptive Neural Asymptotic Tracking Control of Nonlinear Interconnected Systems With Unmodeled Dynamics and Prescribed Performance.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,11.
MLA Cheng, Ting-Ting,et al."Time-/Event-Triggered Adaptive Neural Asymptotic Tracking Control of Nonlinear Interconnected Systems With Unmodeled Dynamics and Prescribed Performance".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2021):11.

入库方式： OAI收割

来源：自动化研究所

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