The quality of an estimated model should be related to the specifications of the intended application. A classical approach is to study the “size” of the asymptotic covariance matrix (the inverse of the Fisher information matrix) of the corresponding parameter vector estimate. In many cases it is possible to design and implement external excitation signals, e.g. pilot signals in communications systems or input signals in control applications. The objective of this seminar is to present some recent advances in optimal experiment design for system identification with a certain application in mind. The idea is to minimize experimental costs (e.g. the energy of the excitation signal), while guaranteeing that the estimated model with a given probability satisfies the specifications of the application. This will result in a convex optimization problem, where the optimal solution should reveal system properties important for the application while hiding irrelevant dynamics. A simple Finite Impulse Response (FIR) example will be used to illustrate the basic ideas. This seminar is based on joint work with Håkan Hjalmarsson, KTH.
Bo Wahlberg received the M.Sc. degree in Electrical Engineering 1983 and the Ph.D. degree in 1987 from Linköping University, Sweden. In December 1991, he became Professor of the Chair of Automatic Control at the Royal Institute of Technology, Stockholm, Sweden. He was a Fulbright visiting professor at Stanford University, August 1997 - July 1998, vice president at KTH 1999 – 2001, and is currently a visiting professor at Information System Lab at Stanford University (August 2009 to June 2010). Bo Wahlberg is a Fellow of the IEEE for his contribution to system identification using orthonormal basis functions.His research interests include system identification, modeling and control of industrial processes, and signal processing with applications in communications and autonomous systems.
