Towards a Scalable Theory of Control

14 Dec
Electrical Engineering Seminar Series
Anders Rantzer, Lund University
Friday, December 14, 2018 - 2:00pm to 3:00pm
Maxwell Dworkin G125

Classical control theory does not scale well for large systems like traffic networks, power networks and chemical reaction networks.  To change this situation, new approaches need to be developed, not only for analysis and synthesis of controllers, but also for modelling and verification.  In this lecture we will present some classes of networked  control problems for which scalable distributed controllers can be proved to achieve the same performance as the best centralized ones.  Both H2- and H-infinity type performance objectives will be considered.  We will also consider classes of problems where distributed controllers cannot achieve the same performance as centralized ones.  For these problems we will show how synthesis and implementation can still be carried out in a scalable manner.  Applications in energy networks and logistics will be briefly discussed.

Speaker Bio: 

Anders Rantzer received a PhD in 1991 from KTH, Stockholm, Sweden.  After postdoctoral positions at KTH and at IMA, University of Minnesota, he joined Lund University in 1993 and was appointed professor of Automatic Control in 1999.  The academic year of 2004/05 he was visiting associate faculty member at Caltech and 2015/16 he was Taylor Family Distinguished Visiting Professor at University of Minnesota. Since 2008 he coordinates the Linnaeus center LCCC at Lund University.

Rantzer is an editorial board member of Proceedings of the IEEE and several other publications.  He is a winner of the SIAM Student Paper Competition, the IFAC Congress Young Author Price and the award for best article in IEE Proceedings - Control Theory & Applications.  He is a Fellow of IEEE, a member of the Royal Swedish Academy of Engineering Sciences and past chairman of the Swedish Scientific Council for Natural and Engineering Sciences.

His research interests are in modeling, analysis and synthesis of control systems, with particular attention to uncertainty, optimization, scalability and adaptation.

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