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

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 auxil...

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Bibliographic Details
Published in:International journal of adaptive control and signal processing Vol. 37; no. 11; pp. 2983 - 3007
Main Authors: Xing, Haoming, Ding, Feng, Pan, Feng, Yang, Erfu
Format: Journal Article
Language:English
Published: Bognor Regis Wiley Subscription Services, Inc 01.11.2023
Subjects:
Algorithms
Computational efficiency
Computing time
Least squares
Parameter estimation
Subsystems
ISSN:0890-6327, 1099-1115
Online Access:Get full text
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Summary: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.
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ISSN:0890-6327
1099-1115
DOI:10.1002/acs.3669