Informativity for data-driven model reduction through interpolation

Research output: Contribution to conferenceAbstractAcademic


A method for data-driven interpolatory model reduction is presented in this extended abstract. This framework enables the computation of the transfer function values at given interpolation points based on time-domain input-output data only, without explicitly identifying the high-order system. Instead, by characterizing the set of all systems explaining the data, necessary and sufficient conditions are given under which all systems in this set share the same transfer function value at a given interpolation point. After following this so-called data informativity perspective, reduced-order models can be obtained by classical interpolation techniques. An example of an electrical circuit illustrates this framework.
Original languageEnglish
PagesExtended abstract
Publication statusPublished - 2020
Event21st IFAC World Congress
- Berlin, Germany
Duration: 11-Jul-202017-Jul-2020


Conference21st IFAC World Congress
Abbreviated titleIFAC WC 2020
Internet address

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