Speaker Prof. Maria Elena Valcher, Department of Information Engineering, University of Padova, Italy
Date｜Time Mar 12 (Wednesday), 2026｜16:30
Zoom https://snu-ac-kr.zoom.us/my/jingyu.lee
Abstract
Unknown inputs related to, e.g., sensor aging, modeling errors, or device bias, represent a major concern in wireless sensor networks, as they degrade the state estimation performance. To overcome this problem, unknown-input observers (UIOs) have been proposed. Most of the available approaches to design UIOs rely upon explicit system models, which can be difficult or impossible to obtain in real-world applications. Data-driven techniques, on the other hand, have become a viable alternative.
In this talk, we will review some recent results about data-driven design of unknown-input observers for linear time-invariant systems, which make use only of data collected offline, without any prior knowledge of the system matrices.
Biography
Elena Valcher (Ph.D. Degree, 1995) is Professor of Control Theory at the University of Padova since 2005. Her current research interests include data-driven methods, distributed estimation, social networks, Boolean control networks, multi-agent systems and consensus problems.
Author of approximately 100 journal papers, 18 book chapters, 120 conference papers and 3 textbooks.
She has been an active volunteer since 1999, serving in various roles for IEEE Control Systems Society, IEEE, IFAC and EUCA.
She was IEEE CSS President in 2015 and the EUCA President (2024-2025). She was a member of the IFAC Technical Board (2017-2023) and she is Chair del 2026 IFAC Major Awards Search Committee.
She was the Founding Editor in Chief of the IEEE Control Systems Letters (2017-2024) and she is co-Editor in Chief of Foundations and Trends in Systems and Control and Editor of Automatica. She was Program Chair of the 2012 IEEE CDC, and co-General Chair of the 2022 IEEE CDC. She is an IEEE Fellow, an IEEE CSS Distinguished Member and an IFAC Fellow.
