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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| Veröffentlicht in: | International journal of adaptive control and signal processing Jg. 37; H. 11; S. 2983 - 3007 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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Bognor Regis
Wiley Subscription Services, Inc
01.11.2023
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| ISSN: | 0890-6327, 1099-1115 |
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| 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. |
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| AbstractList | 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. |
| Author | Pan, Feng Xing, Haoming Ding, Feng Yang, Erfu |
| Author_xml | – sequence: 1 givenname: Haoming surname: Xing fullname: Xing, Haoming organization: Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), School of Internet of Things Engineering Jiangnan University Wuxi China – sequence: 2 givenname: Feng orcidid: 0000-0002-2721-2025 surname: Ding fullname: Ding, Feng organization: Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), School of Internet of Things Engineering Jiangnan University Wuxi China – sequence: 3 givenname: Feng surname: Pan fullname: Pan, Feng organization: Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), School of Internet of Things Engineering Jiangnan University Wuxi China – sequence: 4 givenname: Erfu surname: Yang fullname: Yang, Erfu organization: Department of Design, Manufacturing and Engineering Management University of Strathclyde Glasgow UK |
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| Snippet | Multiple‐input multiple‐output (MIMO) models are widely used in practical engineering. This article derives a new identification model of the MIMO system by... |
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| SubjectTerms | Algorithms Computational efficiency Computing time Least squares Parameter estimation Subsystems |
| Title | Hierarchical recursive least squares parameter estimation methods for multiple‐input multiple‐output systems by using the auxiliary models |
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