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Analytical and Data-based Model Reduction for Nonlinear Systems Based on Differential Balancing


报告时间:2022-6-24 16:00-17:00

腾讯会议ID869482076

密码:201809

报告人:Prof. Jacquelien M. A. Scherpen

工作单位:University of Groningen, the Netherland

举办单位:电气与自动化工程学院

报告人简介:

Jacquelien M. A. Scherpen received her MSc and PhD degrees in 1990 and 1994 from the University of Twente, the Netherlands. She then joined Delft University of Technology and moved in 2006 to the University of Groningen as a professor in Systems and Control Engineering at the Engineering and Technology institute Groningen (ENTEG), fac. Science and Engineering at the University of Groningen, the Netherlands. From 2013 til 2019 she was scientific director of ENTEG. She is currently director of the Groningen Engineering Center, and Captain of Science of the Dutch top sector High Tech Systems and Materials (HTSM).
 Her current research interests include model reduction methods for networks, nonlinear model reduction methods, modeling and control of physical systems with applications to electrical circuits, electromechanical systems, mechanical systems, smart energy networks and distributed optimal control applications to smart grids. She received the 2017-2020 Automatica Best Paper Prize. In 2019 she received a royal distinction and is appointed Knight in the Order of the Netherlands Lion, and she is a fellow of IEEE.
 She is a member of the Board of Governors of the IEEE Control Systems Society, and was chair of the IEEE CSS standing committee on Women in Control in 2020. From 2020 to 2021 she was president of the European Control Association (EUCA).


报告内容简介:

We present a balancing theory for nonlinear systems in the contraction framework. We use prolonged systems to define the controllability and observability functions which can be used for a simultaneous diagonalization procedure, providing a measure for importance of the states. We show that differential balancing has close relationships with the Frechet derivative of the nonlinear Hankel operator. Furthermore, we take a generalized balancing approach in order to have a computationally more feasible method. Error bounds for model reduction by generalized balancing are provided. In addition, we propose an empirical balancing method for nonlinear systems whose input vector fields are constants by utilizing its variational system. The work presented is joint work with Yu Kawano.

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