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Identification for Precision Mechatronics: An Auxiliary Model‐Based Hierarchical Refined Instrumental Variable Algorithm

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Bibliographic Details
Title: Identification for Precision Mechatronics: An Auxiliary Model‐Based Hierarchical Refined Instrumental Variable Algorithm
Authors: Chen Zhang, Yang Liu, Kaixin Liu, Fazhi Song
Source: International Journal of Robust and Nonlinear Control. 35:5026-5042
Publisher Information: Wiley, 2025.
Publication Year: 2025
Description: When the physical properties of mechanical systems align with the structure of the model, the continuous‐time (CT) systems can be effectively represented by an interpretable and parsimonious additive formal models. This article addresses the parameter estimation challenges of additive CT autoregressive moving average (ACTARMA) systems. Based on the maximum likelihood principle, the optimality conditions for the proposed identification algorithms are formulated for ACTARMA systems. Additionally, an auxiliary model‐based hierarchical refined instrumental variable (AM‐HRIV) iterative algorithm and an AM‐HRIV recursive algorithm are developed by means of the hierarchical identification principle and the auxiliary model identification idea. These algorithms establish a pseudo‐linear regression relationship involving optimal prefilters derived from a unified autoregressive moving average model. The effectiveness of the proposed methods is demonstrated by numerical simulation, and the performance of AM‐HRIV iterative method in identifying modal representations is verified by experimental data.
Document Type: Article
Language: English
ISSN: 1099-1239
1049-8923
DOI: 10.1002/rnc.7960
Rights: Wiley Online Library User Agreement
Accession Number: edsair.doi...........5ea02fb7ae3bfa15a56c02a3b2f5c82c
Database: OpenAIRE
Description
Abstract:When the physical properties of mechanical systems align with the structure of the model, the continuous‐time (CT) systems can be effectively represented by an interpretable and parsimonious additive formal models. This article addresses the parameter estimation challenges of additive CT autoregressive moving average (ACTARMA) systems. Based on the maximum likelihood principle, the optimality conditions for the proposed identification algorithms are formulated for ACTARMA systems. Additionally, an auxiliary model‐based hierarchical refined instrumental variable (AM‐HRIV) iterative algorithm and an AM‐HRIV recursive algorithm are developed by means of the hierarchical identification principle and the auxiliary model identification idea. These algorithms establish a pseudo‐linear regression relationship involving optimal prefilters derived from a unified autoregressive moving average model. The effectiveness of the proposed methods is demonstrated by numerical simulation, and the performance of AM‐HRIV iterative method in identifying modal representations is verified by experimental data.
ISSN:10991239
10498923
DOI:10.1002/rnc.7960