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)

[thumbnail of Wei-etal-IJRNC-2022-Overall-recursive-least-squares-and-overall-stochastic-gradient-algorithms-and-their-convergence]
Preview
 Text. Filename: Wei_etal_IJRNC_2022_Overall_recursive_least_squares_and_overall_stochastic_gradient_algorithms_and_their_convergence.pdf
Accepted Author Manuscript
License: Strathprints license 1.0
Download (544kB)| Preview

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 logoORCID: https://orcid.org/0000-0002-9787-4171 and Yang, Erfu ORCID logoORCID: https://orcid.org/0000-0003-1813-5950;
CORE (COnnecting REpositories)