CSpace

Identification and adaptation with binary-valued observations under non-persistent excitation condition

Zhang, Lantian; Zhao, Yanlong; Guo, Lei1
2022-04-01
Source PublicationAUTOMATICA
ISSN0005-1098
Volume138Pages:9
AbstractDynamical 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.
KeywordBinary-valued observation Quasi-Newton algorithm Identification Persistent excitation Martingales Adaptation
DOI10.1016/j.automatica.2022.110158
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation of China[11688101] ; National Natural Science Foundation of China[62025306]
WOS Research AreaAutomation & Control Systems ; Engineering
WOS SubjectAutomation & Control Systems ; Engineering, Electrical & Electronic
WOS IDWOS:000788851300006
PublisherPERGAMON-ELSEVIER SCIENCE LTD
Citation statistics
Document Type期刊论文
Identifierhttp://ir.amss.ac.cn/handle/2S8OKBNM/61289
Collection中国科学院数学与系统科学研究院
Corresponding AuthorGuo, Lei
Affiliation1.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
Recommended Citation
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.
Files in This Item:
There are no files associated with this item.
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Zhang, Lantian]'s Articles
[Zhao, Yanlong]'s Articles
[Guo, Lei]'s Articles
Baidu academic
Similar articles in Baidu academic
[Zhang, Lantian]'s Articles
[Zhao, Yanlong]'s Articles
[Guo, Lei]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Zhang, Lantian]'s Articles
[Zhao, Yanlong]'s Articles
[Guo, Lei]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.