Model order reduction framework for discrete-time systems with error bound via balanced structure

Muhammad Imran and Muhammad Imran

International Journal of Systems Science, 2022, vol. 53, issue 14, 3081-3094

Abstract: Two-dimensional models are challenging to deal with because of their complex structure. As the model's order grows larger, simulation, analysis, design, and control become more challenging. For one-dimensional and two-dimensional models, Enns' weighted (i.e. frequency and time) model reduction procedures result in an unstable reduced-order model. For one-dimensional reduced-order systems, researchers devised a number of frequency-weighted stability-preserving solutions; nevertheless, these solutions result in significant approximation errors. However, to the best of the authors' knowledge, no time-weighted stability-preserving model reduction framework is available in the literature for the discrete-time one-dimensional and two-dimensional systems. In this research, two-dimensional models are separated into two sub-models (i.e. two cascaded one-dimensional models) and reduced using time-weighted Gramians utilising minimal rank-decomposition. The approach applies to both one-dimensional and two-dimensional models. For one-dimensional and two-dimensional models, the suggested methodology additionally gives a priori error-bounds expressions. Numerical results and comparisons to existing methodologies illustrate the efficacy of the proposed methodology.

Date: 2022
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/00207721.2022.2070792 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:tsysxx:v:53:y:2022:i:14:p:3081-3094

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/TSYS20

DOI: 10.1080/00207721.2022.2070792

Access Statistics for this article

International Journal of Systems Science is currently edited by Visakan Kadirkamanathan

More articles in International Journal of Systems Science from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().