Optimal Hankel norm model reduction for discrete-time descriptor systems

X. Cao (Corresponding author), M.B. Saltik (Corresponding author), S. Weiland (Corresponding author)

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

Optimal Hankel norm model reduction for dynamical systems is of great significance in model-based simulation and design. For the class of linear time-invariant systems, it is among the few optimal reduction methods for which a prior error bound between the original system and its approximation is known. However, for descriptor systems, this optimal approximation technique no longer applies. In this paper, we propose several definitions of the Hankel operator for dynamical discrete-time descriptor systems. We investigate the implications of these definitions for the problem of optimal model approximation of descriptor systems in the sense of the Hankel norm. Novel reduction algorithms are derived for this class of systems with and without preservation of the DAE-index. The performance of the proposed methods is illustrated by numerical examples.
LanguageEnglish
Pages4124-4143
JournalJournal of the Franklin Institute
Volume356
Issue number7
Early online dateMar 2019
DOIs
StatePublished - May 2019

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Descriptor Systems
Hankel
Model Reduction
Discrete-time Systems
Norm
Hankel Operator
Optimal systems
Optimal Approximation
Approximation
Reduction Method
Preservation
Error Bounds
Linear Time
Dynamical systems
Dynamical system
Model-based
Numerical Examples
Invariant
Simulation
Class

Keywords

  • Descriptor systems
  • Hankel norm model reduction
  • Finite Hankel matrix approximation
  • Index reduction

Cite this

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