A graph/particle-based method for experiment design in nonlinear systems
2014 (English)In: Proceedings of the 19th IFAC World Congress, 2014 / [ed] Edward Boje and Xiaohua Xia, International Federation of Automatic Control , 2014, 1404-1409 p.Conference paper (Refereed)
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 techniquecan be successfully employed for experiment design in nonlinear systems.
Place, publisher, year, edition, pages
International Federation of Automatic Control , 2014. 1404-1409 p.
, World Congress, ISSN 1474-6670 ; Volumen 19, Part 1
Control Engineering Signal Processing Probability Theory and Statistics
IdentifiersURN: urn:nbn:se:liu:diva-106751DOI: 10.3182/20140824-6-ZA-1003.00361ISBN: 978-3-902823-62-5OAI: oai:DiVA.org:liu-106751DiVA: diva2:718421
19th IFAC World Congress, Cape Town, South Africa, August 24-29
ProjectsProbabilistic modelling of dynamical systems
FunderSwedish Research Council, 621-2013-5524