Tuesday, March 28, 2017 - 4:00pm
Event Calendar Category
LIDS Seminar Series
Building and Room Number
Classical control theory does not scale well for large systems like traffic networks, power networks and chemical reaction networks. However, in this lecture we will present a class of networked control problems for which scalable distributed controllers can be proved to achieve the same performance as the best centralized ones. The control objective is stated in terms of frequency weighted H-infinity norms. This makes it possible to combine disturbance rejection at low frequencies with robustness to high frequency measurement noise and model errors. An optimal controller is given in the form of a multi-variable PI (proportional integrating) controller, which is distributed in the sense that control action along a given network edge is entirely determined by states at nodes connected by that edge. Fundamental bounds on the achievable performance are given in terms of the algebraic connectivity of the network graph.
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 has been associate editor of IEEE Transactions on Automatic Control and several other journals. He is a winner of the SIAM Student Paper Competition, the IFAC Congress Young Author Price and the IET Premium Award for the best article in IEE Proceedings - Control Theory & Applications during 2006. He is a Fellow of IEEE and a member of the Royal Swedish Academy of Engineering Sciences. For the period 2013-15 he was also 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 and distributed control.