Energy-to-peak model reduction for 2-D discrete systems in Fornasini-Marchesini form

Qing Wang, James Lam, Huijun Gao, Qingyang Wang

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)


In this paper, the problem of constructing a reduced-order model to approximate a Fornasini-Marchesini (FM) second model is considered such that the energy-to-peak gain of the error model between the original FM second model and reduced-order one is less than a prescribed scalar. First, a sufficient condition to characterize the bound of the energy-to-peak gain of FM second models is presented in terms of linear matrix inequalities (LMIs). Then, a parameterization of reduced-order models that solve the energy-to-peak model reduction problem is given. Such a problem is formulated in the form of LMIs with inverse constraint. An efficient algorithm is derived to obtain the reduced-order models. Finally, an example is employed to demonstrate the effectiveness of the model reduction algorithm.

Original languageEnglish
Pages (from-to)420-430
Number of pages11
JournalEuropean Journal of Control
Issue number4
Publication statusPublished - 2006
Externally publishedYes


  • Energy-to-peak gain
  • Fornasini-marchesini second model
  • Model reduction

ASJC Scopus subject areas

  • General Engineering


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