Distributed identification based partially-coupled recursive generalized extended least squares algorithm for multivariate input–output-error systems with colored noises from observation data

Qinyao Liu*, Feiyan Chen, Qian Guo, Xuchen Wang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

Abstract

System identification determines the model of the plant from the measurement data and has been widely used in the prediction and control. In this paper, the parameter estimation problem is studied for multivariate equation-error systems with autoregressive moving average noises. Since the considered system is high-dimensional, the distributed identification is considered in this paper. The subsystems are obtained by decomposing the original system in accordance with the number of the outputs. However, the subsystems contain the same parameter vector, resulting in many redundant estimates. By taking the average of the parameter estimation vectors, we develop a partially-coupled subsystem recursive generalized extended least squares (PC-S-RGELS) algorithm to cut down the redundant parameter estimates. In consideration of the information communication between the subsystems and the convergence of the estimation algorithm, a partially-coupled RGELS (PC-RGELS) algorithm is presented by means of the coupling identification concept. Compared with the multivariate RGELS algorithm, the two algorithms have higher computational efficiencies. Finally, an illustrative example is provided to demonstrate the effectiveness of the two proposed algorithms.

Original languageEnglish
Article number115976
JournalJournal of Computational and Applied Mathematics
Volume449
DOIs
Publication statusPublished - 15 Oct 2024

Keywords

  • Coupling identification
  • Decomposition technique
  • Multivariate system
  • Parameter estimation
  • Recursive least squares

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