<p>Identification and adaptation with binary-valued observations under non-persistent excitation condition</p>

doi:10.1016/j.automatica.2022.110158

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	Identification and adaptation with binary-valued observations under non-persistent excitation condition
	Zhang, Lantian ; Zhao, Yanlong ; Guo, Lei 1
	2022-04-01
发表期刊	AUTOMATICA
ISSN	0005-1098
卷号	138 页码:9
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.
关键词	Binary-valued observation Quasi-Newton algorithm Identification Persistent excitation Martingales Adaptation
DOI	10.1016/j.automatica.2022.110158
收录类别	SCI
语种	英语
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
WOS类目	Automation & Control Systems ; Engineering, Electrical & Electronic
WOS记录号	WOS:000788851300006
出版者	PERGAMON-ELSEVIER SCIENCE LTD
引用统计
文献类型	期刊论文
条目标识符	http://ir.amss.ac.cn/handle/2S8OKBNM/61289
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.