We propose an extended method for experiment design in nonlinear state space
models. The proposed input design technique optimizes a scalar cost function of
the information matrix, by computing the optimal stationary probability mass
function (pmf) from which an input sequence is sampled. The feasible set of the
stationary pmf is a polytope, allowing it to be expressed as a convex
combination of its extreme points. The extreme points in the feasible set of
pmf's can be computed using graph theory. Therefore, the final information
matrix can be approximated as a convex combination of the information matrices
associated with each extreme point. For nonlinear systems, the information
matrices for each extreme point can be computed by using particle methods.
Numerical examples show that the proposed technique can be successfully
employed for experiment design in nonlinear systems.Comment: Accepted for publication in the 19th World Congress of the
International Federation of Automatic Control, Cape Town, South Africa. Six
pages, three figure
