Summer 2014 MnDRIVE Seminar Series
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Anders Rantzer,
Lund University
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Abstract
The concept of Integral Quadratic Constraint (IQC) has long been known as a versatile tool for robustness analysis of dynamical systems. Numerous common model imperfections can be efficiently described this way, for example parametric uncertainty, disturbances with bounded frequency content and nonlinear effects such as friction and hysteresis. Based on this idea, rigorous bounds on performance deviations can be computed using semi-definite programming. Computer tools for robustness analysis using IQCs were developed already in the 1990s. However, computational complexity has remained an obstacle for more wide-spread use in applications.
In this presentation, we will discuss a method to drastically improve the computational scalability of IQC analysis, using sparse decomposition of positive definite matrices. This makes it possible to verify stability and performance of large-scale systems with certificates that can be verified individually for each component.
Biosketch
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. Since 2008 he coordinates the Linnaeus center LCCC at Lund University. For the period 2013-15 he is also chairman of the Swedish Scientific Council for Natural and Engineering Sciences.
Prof. 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.
His research interests are in modeling, analysis and synthesis of control systems, with particular attention to uncertainty, optimization and distributed control