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Identification in closed loop: Asymptotic high order variance for restricted complexity models
KTH, Superseded Departments, Signals, Sensors and Systems. (Reglerteknik, Automatic Control)ORCID iD: 0000-0002-9368-3079
University of Newcastle.
1998 (English)Conference paper, Published paper (Refereed)
Abstract [en]

Asymptotic high order variance expressions for identified models have found widespread use in e.g. optimal experiment design, analysis of model accuracy and control. The expressions derived in the 1970's and 1980's by Berk, Ljung and others are valid for a wide range of operating conditions and models such as restricted complexity models identified from open loop data as well as full order models identified from closed loop data. Throughout the 1990's much attention has been devoted to the issue of identification of models based on closed loop data. In order to, at least asymptotically in the model order, be able to analyze the quality of such models a corresponding high order variance theory for restricted complexity models identified from closed loop data is necessary. This paper provides this for models with a fixed noise model. A novel variance expression, valid for Gaussian signals, is derived. Simulations show that this expression is surprisingly accurate also for non-Gaussian signals.

Place, publisher, year, edition, pages
Tampa, FL, USA, 1998. Vol. 3, no Piscataway, NJ, United States, 3396-3401 p.
Keyword [en]
Asymptotic stability, Computational complexity, Control system analysis, Identification (control systems), Mathematical models, High order variance theory, Restricted complexity models, Closed loop control systems
National Category
Control Engineering
Research subject
SRA - ICT
Identifiers
URN: urn:nbn:se:kth:diva-60579DOI: 10.1109/CDC.1998.758226OAI: oai:DiVA.org:kth-60579DiVA: diva2:479500
Note
NR 20140805Available from: 2012-01-17 Created: 2012-01-13 Last updated: 2012-01-17Bibliographically approved

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Hjalmarsson, Håkan

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