Overall recursive least squares and overall stochastic gradient algorithms and their convergence for feedback nonlinear controlled autoregressive systems
Wei, Chun and Zhang, Xiao and Xu, Ling and Ding, Feng and Yang, Erfu (2022) Overall recursive least squares and overall stochastic gradient algorithms and their convergence for feedback nonlinear controlled autoregressive systems. International Journal of Robust and Nonlinear Control, 32 (9). pp. 5534-5554. ISSN 1049-8923 (https://doi.org/10.1002/rnc.6101)
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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.
ORCID iDs
Wei, Chun, Zhang, Xiao, Xu, Ling, Ding, Feng ORCID: https://orcid.org/0000-0002-9787-4171 and Yang, Erfu ORCID: https://orcid.org/0000-0003-1813-5950;-
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Item type: Article ID code: 80209 Dates: DateEvent30 June 2022Published4 April 2022Published Online1 March 2022AcceptedNotes: Please note the change in title from the author's accepted manuscript to the final published version of the article. The title was changed from 'Recursive parameter estimation algorithms and convergence analysis for feedback nonlinear controlled autoregressive systems' is changed to 'Overall recursive least squares and overall stochastic gradient algorithms and their convergence for feedback nonlinear controlled autoregressive systems. Subjects: Technology > Electrical engineering. Electronics Nuclear engineering
Technology > Engineering (General). Civil engineering (General) > Engineering design
Technology > ManufacturesDepartment: Faculty of Engineering > Electronic and Electrical Engineering
Faculty of Engineering > Design, Manufacture and Engineering ManagementDepositing user: Pure Administrator Date deposited: 14 Apr 2022 11:23 Last modified: 28 Nov 2024 08:12 URI: https://strathprints.strath.ac.uk/id/eprint/80209