Hierarchical recursive least squares parameter estimation methods for multiple‐input multiple‐output systems by using the auxiliary models

Haoming Xing, Feng Ding, Feng Pan, Erfu Yang

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)

Abstract

Multiple-input multiple-output (MIMO) models are widely used in practical engineering. This article derives a new identification model of the MIMO system by decomposing the MIMO system into several multiple-input single-output subsystems. By means of the auxiliary model identification idea, an auxiliary model-based recursive least squares (AM-RLS) algorithm is derived for identifying the MIMO systems. In order to reduce the computational burden for identifying MIMO systems, this article presents a hierarchical identification model for the MIMO systems. By applying the hierarchical identification principle, an auxiliary model-based hierarchical least squares (AM-HLS) algorithm is proposed for improving the computational efficiency. The computational efficiency analysis indicates that the AM-HLS algorithm is effective in reducing the calculation amount compared with the AM-RLS algorithm. Moreover, this article analyzes the convergence of the AM-HLS algorithm. The simulation example shows that the AM-RLS and AM-HLS algorithms studied in this article are effective.
Original languageEnglish
Pages (from-to)2983-3007
Number of pages25
JournalInternational Journal of Adaptive Control and Signal Processing
Volume37
Issue number11
Early online date23 Aug 2023
DOIs
Publication statusPublished - Nov 2023

Keywords

  • calculation amount
  • hierarchical identification
  • least squares
  • multivariable system
  • parameter estimation

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