State filtering-based least squares parameter estimation for bilinear systems using the hierarchical identification principle

Tools

Zhang, Xiao and Ding, Feng and Xu, Ling and Yang, Erfu (2018) State filtering-based least squares parameter estimation for bilinear systems using the hierarchical identification principle. IET Control Theory and Applications. pp. 1-10. ISSN 1751-8644 (https://doi.org/10.1049/iet-cta.2018.0156)

[thumbnail of Zhang-etal-IET-CTA-2018-State-filtering-based-least-squares-parameter-estimation]
Preview
 Text. Filename: Zhang_etal_IET_CTA_2018_State_filtering_based_least_squares_parameter_estimation.pdf
Accepted Author Manuscript
Download (389kB)| Preview

Abstract

This study presents a combined parameter and state estimation algorithm for a bilinear system described by its observer canonical state-space model based on the hierarchical identification principle. The Kalman filter is known as the best state filter for linear systems, but not applicable for bilinear systems. Thus, a bilinear state observer (BSO) is designed to give the state estimates using the extremum principle. Then a BSO-based recursive least squares (BSO-RLS) algorithm is developed. For comparison with the BSO-RLS algorithm, by dividing the system into three fictitious subsystems on the basis of the decomposition–coordination principle, a BSO-based hierarchical least squares algorithm is proposed to reduce the computation burden. Moreover, a BSO-based forgetting factor recursive least squares algorithm is presented to improve the parameter tracking capability. Finally, a numerical example illustrates the effectiveness of the proposed algorithms.

ORCID iDs

Zhang, Xiao, Ding, Feng, Xu, Ling and Yang, Erfu ORCID logoORCID: https://orcid.org/0000-0003-1813-5950;
CORE (COnnecting REpositories)