Time: 15:30-16:30, Oct. 18
Abstract:
The Loewner framework for model reduction is extended to the class of linear switched systems. One advantage of this framework is that it introduces a trade-off between accuracy and complexity. Moreover, through this procedure, one can derive state-space models directly from data which is related to the input-output behavior of the original system. Hence, another advantage of the framework is that it does not require the initial system matrices. More exactly, the data used in this framework consists in frequency domain samples of input-output mappings of the original system. The definition of generalized transfer functions for linear switched systems resembles the one for bilinear systems. A key role is played by the coupling matrices, which ensure the transition from one active mode to another.
Bio:
Victor Gosea has been a postdoctoral fellow at Max Planck Institute for Dynamics of Complex Techni-cal Systems in Magdeburg, Germany. His key areas of interest include data-driven system reduction and identification. He has published lots of manuscripts in peer-reviewed journals.
Professional Experience:
2016.03 - 2016.05 Visiting Research Assistant, Rice University, Houston, USA
2013.09 - 2017 Ph.D, Electrical Engineering, Jacobs University Bremen, Bremen, Germany
2011 - 2013 M.S, Communications, Systems and Electronics, Jacobs University Bremen, Bremen, Germany
2008 - 2011 B.S, Jacobs University Bremen, Bremen, Germany, 'Carol I' National College, Craiova, Romania
Sist seminar 18210
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