Hierarchical least squares parameter estimation algorithm and the convergence analysis on the two-input error system

Fang  Qiu; Haixu  Zhang; Yan  Ji

doi:10.6180/jase.202412_27(12).0014

Hierarchical least squares parameter estimation algorithm and the convergence analysis on the two-input error system

Fang QiuThis email address is being protected from spambots. You need JavaScript enabled to view it., Haixu Zhang, and Yan Ji

College of Science, Shandong University of Aeronautics, Binzhou 256600, China

Received: September 9, 2023
Accepted: January 14, 2024
Publication Date: March 2, 2024

Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

Download Citation: ||https://doi.org/10.6180/jase.202412_27(12).0014

This paper mainly studies the identification problem of the single-output two-input error system. A hierarchical least squares algorithm is derived by combining the model decomposition skill and the principle of hierarchical identification. The basic idea of the derived identification method is to decompose the system into two subsystems and identify the parameter vector of each subsystem separately. Compared with the auxiliary model least squares (AM-RLS) algorithm, the proposed two-stage recursive least squares (TS-RLS) algorithm has lower computational cost and higher estimation accuracy. Furthermore, the convergence of the TS-RLS algorithm is analyzed, which can guarantee the stability of the algorithm and make it more suitable for various practical application scenarios. Finally, a numerical example is presented to illustrate the validity of the derived approach.

Keywords: Error class system; Ancillary model; Least squares method; Hierarchical least squares algorithm

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