Analyzing Iterations in Identification with application to Nonparametric H-infinity-norm Estimation

T.A.E. Oomen, C.R. Rojas, H. Hjalmarsson, B. Wahlberg

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

2 Citations (Scopus)
126 Downloads (Pure)


In the last decades, many iterative approaches in the field of system identification for control have been proposed. Many successful implementations have been reported, despite the lack of a solid analysis with respect to the convergence and value of these iterations. The aim of this paper is to present a thorough analysis of a specific iterative algorithm that involves nonparametric H8-norm estimation. The pursued approach involves a novel frequency domain approach that appropriately deals with additive stochastic disturbances and input normalization. The results of the novel convergence analysis are twofold: i) the presence of additive disturbances introduces a bias in the estimation procedure, and ii) the iterative procedure can be interpreted as experiment design for H8-norm estimation, revealing the value of iterations and limits of accuracy in terms of the Fisher information matrix. The results are confirmed by means of a simulation example.
Original languageEnglish
Title of host publicationProceedings of the 18th IFAC World Congress, August 28 - September 2, 2011, Milano, Italy
Place of PublicationOxford
Publication statusPublished - 2011


Dive into the research topics of 'Analyzing Iterations in Identification with application to Nonparametric H-infinity-norm Estimation'. Together they form a unique fingerprint.

Cite this