 
Summary: The Center for Control, Dynamical Systems, and Computation
University of California at Santa Barbara
Spring 2009 Seminar Series
Presents
Filter Design from Data: a New Approach to Filter Design
for Nonlinear Systems
Mario Milanese
Politecnico di Torino
Thursday, April 23, 2009 3:00  4:00pm ESB 2001
Abstract: In the talks, we consider the problem of designing a filter that, operating on noisy measurements of input u
and output y of a dynamical system, gives estimates (possibly optimal in some sense) of some other variable of interest z. A
large body of literature exists, which investigates this problem assuming that the systems equations relating u, y and z are
known. However, in most practical situations, the systems equations are not (completely) known, but a data set composed
of noisy measurements of u, y and z are available. In such situations, a twostep procedure is typically adopted: a model
is identified from the set of measured data, and the filter is designed on the basis of the identified model. In this seminar
we propose and alternative solution, which uses the available data set of measured u, y and z not for the identification of
system dynamics, but for the direct design of filter. Such a direct design is investigated within both the ParametricStatistical
(PS) and Nonlinear (NSM) frameworks. In the PS framework, the noises are assumed to be stochastic and optimality refers
to minimizing the estimation error variance. It is shown that the direct design has superior features in terms of estimation
error variance, especially in the presence of modeling errors. Another relevant advantage of the direct design over the two
