Adaptive Neural Output-Feedback Controller Design of Switched Nonlower Triangular Nonlinear Systems With Time Delays
文献类型:期刊论文
| 作者 | Niu, Ben2; Wang, Ding1,3 ; Liu, Ming4 ; Song, Xinmin2; Wang, Huanqing5; Duan, Peiyong2
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| 刊名 | IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
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| 出版日期 | 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. |
| WOS关键词 | H-INFINITY CONTROL ; FUZZY CONTROL ; STABILIZATION ; STABILITY ; TRACKING |
| 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研究方向 | Computer Science ; Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:000576436600025 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 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 |
| 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.Chinese Acad Sci, State Key Lab Management & Control Complex Syst, Inst Automat, Beijing 100190, Peoples R China 2.Shandong Normal Univ, Sch Informat Sci & Engn, Jinan 250014, Peoples R China 3.Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China 4.Harbin Inst Technol, Sch Astronaut, Harbin 150001, Peoples R China 5.Bohai Univ, Coll Math & Phys, Jinzhou 121013, 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. |
入库方式: OAI收割
来源:自动化研究所
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