Abstract

We will present a compositional approach that uncovers the dynamical properties of a networked system by exploiting input-output properties of the subsystems and the structure of their interconnection. This approach overcomes the complexity of a large-scale, nonlinear network model by abstracting important input-output properties from more detailed dynamical models describing the subsystems.  We will illustrate this approach with examples from synthetic biology, multi-agent systems, and traffic networks. These examples will also serve to exhibit special interconnection structures and input-output properties that are compatible with these structures. We will then present a large-scale optimization technique to search for input-output properties that certify a prescribed network performance criterion for a given interconnection.
Biosketch

Murat Arcak is a professor at UC Berkeley in the Electrical Engineering and Computer Sciences Department. He received a B.S. degree in Electrical Engineering from the Bogazici University, Istanbul, Turkey (1996) and M.S. and Ph.D. degrees from the University of California, Santa Barbara (1997 and 2000). His research is in dynamical systems and control theory with applications to synthetic biology and multi-agent systems. He received a CAREER Award from the National Science Foundation in 2003, the Donald P. Eckman Award from the American Automatic Control Council in 2006, and the Control and Systems Theory Prize from the Society for Industrial and Applied Mathematics (SIAM) in 2007. He is a member of SIAM and a fellow of IEEE.

URL: http://www.eecs.berkeley.edu/Faculty/Homepages/arcak.html