Model Reduction by Moment Matching for Convergent Lur'e-Type Models

M.F. Shakib, G. Scarciotti, A.Y. Pogromsky, A. Pavlov, N. van de Wouw

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

3 Citations (Scopus)


This paper proposes an approach to model order reduction of convergent Lur'e-type models, which consist of a linear time-invariant (LTI) block and a static nonlinear block that is placed in feedback with the LTI block. In the proposed approach, we match a finite number of moments of the LTI block and keep the static nonlinear block to approximate the moments of the Lur'e-type model. The benefits of this approach are that the Lur'e-type structure is preserved after reduction, that the reduction method has an interpretation in terms of the frequency response function of the LTI block and that global exponential stability properties of the full-order model are preserved. The effectiveness of the approach is illustrated in a numerical example.

Original languageEnglish
Title of host publication2021 American Control Conference (ACC)
PublisherInstitute of Electrical and Electronics Engineers
Number of pages6
ISBN (Electronic)9781665441971
Publication statusPublished - 28 Jul 2021
Event2021 American Control Conference, ACC 2021 - Virtual, Virtual, New Orleans, United States
Duration: 25 May 202128 May 2021


Conference2021 American Control Conference, ACC 2021
Abbreviated titleACC 2021
Country/TerritoryUnited States
CityVirtual, New Orleans
Internet address


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