Adaptive Neural State-Feedback Tracking Control of Stochastic Nonlinear Switched Systems: An Average Dwell-Time Method
文献类型：期刊论文
| 作者 | Niu, Ben1,2; Wang, Ding3 ; Alotaibi, Naif D.4; Alsaadi, Fuad E.4
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| 刊名 | IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
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| 出版日期 | 2019-04-01 |
| 卷号 | 30期号:4页码:1076-1087 |
| 关键词 | Adaptive tracking control average dwell time (ADT) neural networks (NNs) nonstrict-feedback structure stochastic nonlinear systems switched nonlinear systems |
| ISSN号 | 2162-237X |
| DOI | 10.1109/TNNLS.2018.2860944 |
| 通讯作者 | Niu, Ben(niubenbhu@163.com) |
| 英文摘要 | In this paper, the problem of adaptive neural state-feedback tracking control is considered for a class of stochastic nonstrict-feedback nonlinear switched systems with completely unknown nonlinearities. In the design procedure, the universal approximation capability of radial basis function neural networks is used for identifying the unknown compounded nonlinear functions, and a variable separation technique is employed to overcome the design difficulty caused by the nonstrict-feedback structure. The most outstanding novelty of this paper is that individual Lyapunov function of each subsystem is constructed by flexibly adopting the upper and lower bounds of the control gain functions of each subsystem. Furthermore, by combining the average dwell-time scheme and the adaptive backstepping design, a valid adaptive neural state-feedback controller design algorithm is presented such that all the signals of the switched closed-loop system are in probability semiglobally uniformly ultimately bounded, and the tracking error eventually converges to a small neighborhood of the origin in probability. Finally, the availability of the developed control scheme is verified by two simulation examples. |
| WOS关键词 | H-INFINITY CONTROL ; PRESCRIBED PERFORMANCE ; GLOBAL STABILIZATION ; STABILITY ; DESIGN ; FORM |
| 资助项目 | National Natural Science Foundation of China[61773192] ; National Natural Science Foundation of China[61773246] |
| WOS研究方向 | Computer Science ; Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:000461854100009 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 资助机构 | National Natural Science Foundation of China |
| 源URL | [http://ir.ia.ac.cn/handle/173211/28082]  |
| 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.Shandong Normal Univ, Sch Informat Sci & Engn, Jinan 250014, Shandong, Peoples R China 2.City Univ Hong Kong, Dept Mech & Biomed Engn, Hong Kong, Peoples R China 3.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 4.King Abdulaziz Univ, Fac Engn, Dept Elect & Comp Engn, Jeddah 21589, Saudi Arabia |
| 推荐引用方式 GB/T 7714 | Niu, Ben,Wang, Ding,Alotaibi, Naif D.,et al. Adaptive Neural State-Feedback Tracking Control of Stochastic Nonlinear Switched Systems: An Average Dwell-Time Method[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2019,30(4):1076-1087. |
| APA | Niu, Ben,Wang, Ding,Alotaibi, Naif D.,&Alsaadi, Fuad E..(2019).Adaptive Neural State-Feedback Tracking Control of Stochastic Nonlinear Switched Systems: An Average Dwell-Time Method.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,30(4),1076-1087. |
| MLA | Niu, Ben,et al."Adaptive Neural State-Feedback Tracking Control of Stochastic Nonlinear Switched Systems: An Average Dwell-Time Method".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 30.4(2019):1076-1087. |
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来源：自动化研究所
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