Development of Frequency Weighted Model Order Reduction Techniques for Discrete-Time One-Dimensional and Two-Dimensional Linear Systems With Error Bounds
Frequency weighted model reduction framework pretested by Enns yields an unstable reduced order model. Researchers demonstrated several stability preserving techniques to address this main shortcoming, ensuring the stability of one-dimensional and two-dimensional reduced-order systems; nevertheless,...
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9693532/ |
| _version_ | 1818324116263927808 |
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| author | Muhammad Imran Muhammad Imran Mian Ilyas Ahmad |
| author_facet | Muhammad Imran Muhammad Imran Mian Ilyas Ahmad |
| author_sort | Muhammad Imran |
| collection | DOAJ |
| description | Frequency weighted model reduction framework pretested by Enns yields an unstable reduced order model. Researchers demonstrated several stability preserving techniques to address this main shortcoming, ensuring the stability of one-dimensional and two-dimensional reduced-order systems; nevertheless, these approaches produce significant truncation errors. In this article, Gramians-based frequency weighted model order reduction frameworks have been presented for the discrete-time one-dimensional and two-dimensional systems. Proposed approaches overcome Enns’ main shortcoming in reduced-order model instability. In comparison to the various stability-preserving approaches, proposed frameworks provide an easily measurable <italic>a priori</italic> error-bound expression. The simulation results show that proposed frameworks perform well in comparison to other existing stability-preserving strategies, demonstrating the efficacy of proposed frameworks. |
| first_indexed | 2024-12-13T11:23:28Z |
| format | Article |
| id | doaj.art-7586fed8595f43118467a60cca8bff41 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-13T11:23:28Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-7586fed8595f43118467a60cca8bff412022-12-21T23:48:22ZengIEEEIEEE Access2169-35362022-01-0110150961511710.1109/ACCESS.2022.31463949693532Development of Frequency Weighted Model Order Reduction Techniques for Discrete-Time One-Dimensional and Two-Dimensional Linear Systems With Error BoundsMuhammad Imran0https://orcid.org/0000-0003-1001-9648Muhammad Imran1Mian Ilyas Ahmad2Department of Electrical Engineering, Military College of Signals (MCS), National University of Sciences and Technology (NUST), Islamabad, PakistanDepartment of Electrical Engineering, Military College of Signals (MCS), National University of Sciences and Technology (NUST), Islamabad, PakistanDepartment of Computational Engineering, Research Centre for Modelling and Simulation (RCMS), National University of Sciences and Technology (NUST), Islamabad, PakistanFrequency weighted model reduction framework pretested by Enns yields an unstable reduced order model. Researchers demonstrated several stability preserving techniques to address this main shortcoming, ensuring the stability of one-dimensional and two-dimensional reduced-order systems; nevertheless, these approaches produce significant truncation errors. In this article, Gramians-based frequency weighted model order reduction frameworks have been presented for the discrete-time one-dimensional and two-dimensional systems. Proposed approaches overcome Enns’ main shortcoming in reduced-order model instability. In comparison to the various stability-preserving approaches, proposed frameworks provide an easily measurable <italic>a priori</italic> error-bound expression. The simulation results show that proposed frameworks perform well in comparison to other existing stability-preserving strategies, demonstrating the efficacy of proposed frameworks.https://ieeexplore.ieee.org/document/9693532/Model reductionminimal realizationHankel-Singular valuesoptimal Hankel norm approximationfrequency response errorerror bound |
| spellingShingle | Muhammad Imran Muhammad Imran Mian Ilyas Ahmad Development of Frequency Weighted Model Order Reduction Techniques for Discrete-Time One-Dimensional and Two-Dimensional Linear Systems With Error Bounds IEEE Access Model reduction minimal realization Hankel-Singular values optimal Hankel norm approximation frequency response error error bound |
| title | Development of Frequency Weighted Model Order Reduction Techniques for Discrete-Time One-Dimensional and Two-Dimensional Linear Systems With Error Bounds |
| title_full | Development of Frequency Weighted Model Order Reduction Techniques for Discrete-Time One-Dimensional and Two-Dimensional Linear Systems With Error Bounds |
| title_fullStr | Development of Frequency Weighted Model Order Reduction Techniques for Discrete-Time One-Dimensional and Two-Dimensional Linear Systems With Error Bounds |
| title_full_unstemmed | Development of Frequency Weighted Model Order Reduction Techniques for Discrete-Time One-Dimensional and Two-Dimensional Linear Systems With Error Bounds |
| title_short | Development of Frequency Weighted Model Order Reduction Techniques for Discrete-Time One-Dimensional and Two-Dimensional Linear Systems With Error Bounds |
| title_sort | development of frequency weighted model order reduction techniques for discrete time one dimensional and two dimensional linear systems with error bounds |
| topic | Model reduction minimal realization Hankel-Singular values optimal Hankel norm approximation frequency response error error bound |
| url | https://ieeexplore.ieee.org/document/9693532/ |
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