Identification and adaptation with binary-valued observations under non-persistent excitation condition
文献类型：期刊论文
| 作者 | Zhang, Lantian; Zhao, Yanlong; Guo, Lei1 |
| 刊名 | AUTOMATICA
 |
| 出版日期 | 2022-04-01 |
| 卷号 | 138页码:9 |
| 关键词 | Binary-valued observation Quasi-Newton algorithm Identification Persistent excitation Martingales Adaptation |
| ISSN号 | 0005-1098 |
| DOI | 10.1016/j.automatica.2022.110158 |
| 英文摘要 | Dynamical systems with binary-valued observations are widely used in information industry, technology of biological pharmacy and other fields. Though there have been much efforts devoted to the identification of such systems, most of the previous investigations are based on first-order gradient algorithm which usually has much slower convergence rate than the Quasi-Newton algorithm. Moreover, persistence of excitation (PE) conditions are usually required to guarantee consistent parameter estimates in the existing literature, which are hard to be verified or guaranteed for feedback control systems. In this paper, we propose an online projected Quasi-Newton type algorithm for parameter estimation of stochastic regression models with binary-valued observations and varying thresholds. By using both the stochastic Lyapunov function and martingale estimation methods, we establish the strong consistency of the estimation algorithm and provide the convergence rate, under a signal condition which is considerably weaker than the traditional PE condition and coincides with the weakest possible excitation known for the classical least square algorithm of stochastic regression models. Convergence of adaptive predictors and their applications in adaptive control are also discussed. (C)& nbsp;2022 Elsevier Ltd. All rights reserved. |
| 资助项目 | National Natural Science Foundation of China[11688101] ; National Natural Science Foundation of China[62025306] |
| WOS研究方向 | Automation & Control Systems ; Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:000788851300006 |
| 出版者 | PERGAMON-ELSEVIER SCIENCE LTD |
| 源URL | [http://ir.amss.ac.cn/handle/2S8OKBNM/61288]  |
| 专题 | 中国科学院数学与系统科学研究院 |
| 通讯作者 | Guo, Lei |
| 作者单位 | 1.Chinese Acad Sci, Acad Math & Syst Sci, Key Lab Syst & Control, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Sch Math Sci, Beijing 100049, Peoples R China |
| 推荐引用方式 GB/T 7714 |
Zhang, Lantian,Zhao, Yanlong,Guo, Lei. Identification and adaptation with binary-valued observations under non-persistent excitation condition [J]. AUTOMATICA,2022,138:9. |
| APA |
Zhang, Lantian,Zhao, Yanlong,&Guo, Lei.(2022). Identification and adaptation with binary-valued observations under non-persistent excitation condition .AUTOMATICA,138,9. |
| MLA |
Zhang, Lantian,et al." Identification and adaptation with binary-valued observations under non-persistent excitation condition ".AUTOMATICA 138(2022):9. |
入库方式： OAI收割
来源：数学与系统科学研究院
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