A graph/particle-based method for experiment design in nonlinear systems
2014 (English)Conference paper (Refereed)
We present an extended method for experiment design in nonlinear state space models. The proposed input design method 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.
Place, publisher, year, edition, pages
2014. Paper MoB14.1- p.
System identification, input design, particle filter, nonlinear systems
Research subject Electrical Engineering
IdentifiersURN: urn:nbn:se:kth:diva-158126ScopusID: 2-s2.0-84929742368OAI: oai:DiVA.org:kth-158126DiVA: diva2:774584
The 19th IFAC World Congress, 19th World Congress of the International Federation of Automatic Control, Cape Town, South Africa, 24-29 August 2014
FunderSwedish Research Council, 621-2013-5524Swedish Research Council, 621-2011-5890Swedish Research Council, 621-2009-4017EU, European Research Council, 267381
QC 201501152014-12-262014-12-262015-10-12Bibliographically approved