Model reduction by matching zero-order moments for 2-D discrete systems
File(s)CDC_2023___MM_for_2_D_Systems (2).pdf (598.67 KB)
Accepted version
Author(s)
Mao, Junyu
Scarciotti, Giordano
Type
Conference Paper
Abstract
Ahstract-In this paper, the problem of model reduction for two-dimensional (2-D) systems in the Fornasini-Marchesini local state-space form is addressed by matching zero-order moments. Two characterizations of zero-order moments are proposed: the first based on the notion of interpolation of complex points and the second based on the concept of steady state. A parameterized family of reduced-order models that achieves moment matching while preserving the 2-D structure of the original system is presented. The developed theory is illustrated by means of a 2-D low-pass filter reduction problem.
Date Issued
2023-12-13
Date Acceptance
2023-07-12
Citation
2023 62nd IEEE Conference on Decision and Control (CDC), 2023
Publisher
IEEE
Journal / Book Title
2023 62nd IEEE Conference on Decision and Control (CDC)
Copyright Statement
Copyright © 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Identifier
http://dx.doi.org/10.1109/cdc49753.2023.10383240
Source
2023 62nd IEEE Conference on Decision and Control (CDC)
Publication Status
Published
Start Date
2023-12-13
Finish Date
2023-12-15
Coverage Spatial
Singapore, Singapore