Neural Adaptive Event-Triggered Control for Nonlinear Uncertain Stochastic Systems With Unknown Hysteresis
文献类型：期刊论文
| 作者 | Wang, Jianhui1,4; Liu, Zhi4; Zhang, Yun4; Chen, C. L. Philip2,3,5 |
| 刊名 | IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
 |
| 出版日期 | 2019-11-01 |
| 卷号 | 30期号:11页码:3300-3312 |
| 关键词 | Actuators Hysteresis Nonlinear systems Artificial neural networks Adaptive systems Stochastic systems System performance Actuator failure adaptive control event-triggered neural networks (NNs) stochastic nonlinear systems unknown direction hysteresis |
| ISSN号 | 2162-237X |
| DOI | 10.1109/TNNLS.2018.2890699 |
| 通讯作者 | Liu, Zhi(lz@gdut.edu.cn) |
| 英文摘要 | In this paper, the uncertain direct of the hysteretic system component will be considered. Besides, the effect of stochastic disturbance inevitably exists in many practical systems, which would cause the instability. Simultaneously, it is significant to guarantee the perfect error tracking performance for the uncertain nonlinear hysteresis systems when operation suffers the failure. To ensure the maintaining acceptable system performance in reality, the new properties of the Nussbaum function are proposed, and an auxiliary virtual controller is designed through the neural network (NN) universal approximator. Furthermore, it is challenged to save the system-limited transmutation resource for nonlinear systems, especially for stochastic nonlinear systems, with unknown hysteresis input and actuator failures. The coupling effect of the system communication resource constrains has to arise the issue of the mutual coupling function, which makes that the tracking control design is more complicated. Using the proposed event-triggered controller and back-stepping technology, a new optimization algorithm is proposed to ensure that the states of the closed-loop system and the tracking error remain bounded in probability. Finally, to illustrate the effectiveness of our proposed adaptive NN control method with the event-triggered strategy, some numerical examples are provided. |
| WOS关键词 | ACTUATOR FAILURE COMPENSATION ; OUTPUT-FEEDBACK CONTROL ; NETWORKED SYSTEMS ; BACKSTEPPING CONTROL ; DELAY SYSTEMS ; DEAD-ZONE ; STABILIZATION |
| 资助项目 | National Natural Science Foundation of China[61573108] ; Natural Science Foundation of Guangdong Province[2016A030313715] ; Guangdong Province Universities and Colleges Pearl River Scholar Funded Scheme |
| WOS研究方向 | Computer Science ; Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:000494702100007 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 资助机构 | National Natural Science Foundation of China ; Natural Science Foundation of Guangdong Province ; Guangdong Province Universities and Colleges Pearl River Scholar Funded Scheme |
| 源URL | [http://ir.ia.ac.cn/handle/173211/28858]  |
| 专题 | 离退休人员 |
| 通讯作者 | Liu, Zhi |
| 作者单位 | 1.Guangzhou Univ, Sch Mech & Elect Engn, Guangzhou 510006, Guangdong, Peoples R China 2.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100080, Peoples R China 3.Dalian Maritime Univ, Maritime Coll, Dalian 116026, Peoples R China 4.Guangdong Univ Technol, Sch Automat, Guangzhou 510006, Guangdong, Peoples R China 5.Univ Macau, Fac Sci & Technol, Dept Comp & Informat Sci, Macau 99999, Peoples R China |
| 推荐引用方式 GB/T 7714 | Wang, Jianhui,Liu, Zhi,Zhang, Yun,et al. Neural Adaptive Event-Triggered Control for Nonlinear Uncertain Stochastic Systems With Unknown Hysteresis[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2019,30(11):3300-3312. |
| APA | Wang, Jianhui,Liu, Zhi,Zhang, Yun,&Chen, C. L. Philip.(2019).Neural Adaptive Event-Triggered Control for Nonlinear Uncertain Stochastic Systems With Unknown Hysteresis.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,30(11),3300-3312. |
| MLA | Wang, Jianhui,et al."Neural Adaptive Event-Triggered Control for Nonlinear Uncertain Stochastic Systems With Unknown Hysteresis".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 30.11(2019):3300-3312. |
入库方式： OAI收割
来源：自动化研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。