Overall recursive least squares and overall stochastic gradient algorithms and their convergence for feedback nonlinear controlled autoregressive systems

This article deals with the problems of the parameter estimation for feedback nonlinear controlled autoregressive systems (i.e., feedback nonlinear equation‐error systems). The bilinear‐in‐parameter identification model is formulated to describe the feedback nonlinear system. An overall recursive le...

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Vydáno v:	International journal of robust and nonlinear control Ročník 32; číslo 9; s. 5534 - 5554
Hlavní autoři:	Wei, Chun, Zhang, Xiao, Xu, Ling, Ding, Feng, Yang, Erfu
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Bognor Regis Wiley Subscription Services, Inc 01.06.2022
Témata:	Algorithms bilinear‐in‐parameter model Control systems Convergence convergence analysis Feedback feedback nonlinear system gradient search Least squares Nonlinear control Nonlinear equations Nonlinear systems Parameter estimation Parameter identification Stochastic processes
ISSN:	1049-8923, 1099-1239
On-line přístup:	Získat plný text
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Abstract	This article deals with the problems of the parameter estimation for feedback nonlinear controlled autoregressive systems (i.e., feedback nonlinear equation‐error systems). The bilinear‐in‐parameter identification model is formulated to describe the feedback nonlinear system. An overall recursive least squares algorithm is developed to handle the difficulty of the bilinear‐in‐parameter. For the purpose of avoiding the heavy computational burden, an overall stochastic gradient algorithm is deduced and the forgetting factor is introduced to improve the convergence rate. Furthermore, the convergence analysis of the proposed algorithms are established by means of the stochastic process theory. The effectiveness of the proposed algorithms are illustrated by the simulation example.
AbstractList	This article deals with the problems of the parameter estimation for feedback nonlinear controlled autoregressive systems (i.e., feedback nonlinear equation‐error systems). The bilinear‐in‐parameter identification model is formulated to describe the feedback nonlinear system. An overall recursive least squares algorithm is developed to handle the difficulty of the bilinear‐in‐parameter. For the purpose of avoiding the heavy computational burden, an overall stochastic gradient algorithm is deduced and the forgetting factor is introduced to improve the convergence rate. Furthermore, the convergence analysis of the proposed algorithms are established by means of the stochastic process theory. The effectiveness of the proposed algorithms are illustrated by the simulation example.
Author	Zhang, Xiao Ding, Feng Wei, Chun Yang, Erfu Xu, Ling
Author_xml	– sequence: 1 givenname: Chun orcidid: 0000-0002-7318-4563 surname: Wei fullname: Wei, Chun organization: Jiangnan University – sequence: 2 givenname: Xiao orcidid: 0000-0002-6413-6148 surname: Zhang fullname: Zhang, Xiao email: xzhang13@126.com, xzhang@jiangnan.edu.cn organization: Jiangnan University – sequence: 3 givenname: Ling orcidid: 0000-0002-5040-5634 surname: Xu fullname: Xu, Ling organization: Jiangnan University – sequence: 4 givenname: Feng orcidid: 0000-0002-2721-2025 surname: Ding fullname: Ding, Feng organization: Hubei University of Technology – sequence: 5 givenname: Erfu surname: Yang fullname: Yang, Erfu organization: University of Strathclyde
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Snippet	This article deals with the problems of the parameter estimation for feedback nonlinear controlled autoregressive systems (i.e., feedback nonlinear...
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SubjectTerms	Algorithms bilinear‐in‐parameter model Control systems Convergence convergence analysis Feedback feedback nonlinear system gradient search Least squares Nonlinear control Nonlinear equations Nonlinear systems Parameter estimation Parameter identification Stochastic processes
Title	Overall recursive least squares and overall stochastic gradient algorithms and their convergence for feedback nonlinear controlled autoregressive systems
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