Structure‐Preserving Model Reduction Approach for Structured Index‐2 Descriptor‐Systems

ABSTRACT This article presents a balancing‐based algorithm for reducing the complexity of structured discrete‐time linear time‐invariant (DT‐LTI) index‐2 descriptor systems. The proposed algorithm involves projecting the index‐2 system onto a hidden manifold, which converts it into a generalized system. However, this causes the system to lose its sparsity and become dense, which is impractical for large‐scale systems. To overcome this issue, the authors enhance the Smith‐based iterative method for solving discrete‐time algebraic Lyapunov equations, which allow for balanced truncation without explicitly forming the dense system. The proposed algorithm is shown to be efficient and robust through numerical simulations.

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Publication Details

Journal
Numerical Linear Algebra with Applications
Published
2024-12-16
DOI
https://doi.org/10.1002/nla.2609
Primary Topic
Model Reduction and Neural Networks
Type
article
Field-Weighted Citation Impact
0.00

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article

Structure‐Preserving Model Reduction Approach for Structured Index‐2 Descriptor‐Systems

Sufi Galib Omar, Ekram Hossain Khan, Mohammad‐Sahadet Hossain, M. Monir Uddin
Numerical Linear Algebra with Applications
Model Reduction and Neural Networks
article

Structure‐Preserving Model Reduction Approach for Structured Index‐2 Descriptor‐Systems

Sufi Galib Omar, Ekram Hossain Khan, Mohammad‐Sahadet Hossain, M. Monir Uddin
article en

Abstract

ABSTRACT This article presents a balancing‐based algorithm for reducing the complexity of structured discrete‐time linear time‐invariant (DT‐LTI) index‐2 descriptor systems. The proposed algorithm involves projecting the index‐2 system onto a hidden manifold, which converts it into a generalized system. However, this causes the system to lose its sparsity and become dense, which is impractical for large‐scale systems. To overcome this issue, the authors enhance the Smith‐based iterative method for solving discrete‐time algebraic Lyapunov equations, which allow for balanced truncation without explicitly forming the dense system. The proposed algorithm is shown to be efficient and robust through numerical simulations.

Numerical Linear Algebra with ApplicationsVol. 32(1)
North South University (BD)
North South University
Openalex Percentile: Top 78%
Model Reduction and Neural Networks
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