Reduced-order state-space models for two-dimensional discrete systems via bivariate discrete orthogonal polynomials
Zhao-Hong Wang,
Yao-Lin Jiang and
Kang-Li Xu
Mathematics and Computers in Simulation (MATCOM), 2023, vol. 212, issue C, 441-456
Abstract:
This paper investigates the new model order reduction (MOR) methods via bivariate discrete orthogonal polynomials for two-dimensional (2-D) discrete systems. The 2-D discrete system is described by the Kurek model. First, we deduce algebraically the shift-transformation matrix of the classical discrete orthogonal polynomials of one variable. By means of the shift-transformation matrices, 2-D discrete systems are expanded in the spaces spanned by bivariate discrete orthogonal polynomials. The coefficient matrices are calculated from matrix equations. Then the reduced-order systems are produced by the orthogonal projection matrices defined by the coefficient matrices. Theoretical analysis shows that the reduced-order systems can match a certain number of coefficient vectors of the original outputs. Finally, one numerical example is simulated to demonstrate the feasibility and effectiveness of the proposed methods.
Keywords: Two-dimensional discrete systems; Model order reduction; Bivariate discrete orthogonal polynomials (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:eee:matcom:v:212:y:2023:i:c:p:441-456
DOI: 10.1016/j.matcom.2023.05.009
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