Input design as a tool to improve the convergence of PEM
2013 (English)In: Automatica, ISSN 0005-1098, Vol. 49, no 11, 3282-3291 p.Article in journal (Refereed) Published
The Prediction Error Method (PEM) is related to an optimization problem built on input/output data collected from the system to be identified. It is often hard to find the global solution of this optimization problem because the corresponding objective function presents local minima and/or the search space is constrained to a nonconvex set. The shape of the cost function, and hence the difficulty in solving the optimization problem, depends directly on the experimental conditions, more specifically on the spectrum of the input/output data collected from the system. Therefore, it seems plausible to improve the convergence to the global minimum by properly choosing the spectrum of the input; in this paper, we address this problem. We present a condition for convergence to the global minimum of the cost function and propose its inclusion in the input design. We present the application of the proposed approach to case studies where the algorithms tend to get trapped in nonglobal minima.
Place, publisher, year, edition, pages
2013. Vol. 49, no 11, 3282-3291 p.
Identification methods, Experiment design
IdentifiersURN: urn:nbn:se:kth:diva-134727DOI: 10.1016/j.automatica.2013.08.027ISI: 000326553300013ScopusID: 2-s2.0-84885573918OAI: oai:DiVA.org:kth-134727DiVA: diva2:668264
FunderEU, European Research Council, 267381Swedish Research Council, 621-2009-4017
QC 201406182013-11-292013-11-282014-06-18Bibliographically approved