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Sliding window recursive quadratic optimization with variable regularization
Dept. of Mech. Eng., Univ. of Kentucky, Lexington, KY, USA.
Karlstad University, Faculty of Technology and Science, Department of Physics and Electrical Engineering.
Karlstad University, Faculty of Technology and Science, Department of Physics and Electrical Engineering.
2011 (English)In: American Control Conference (ACC), 2011, San Francisco: IEEE conference proceedings, 2011, 3275-3280 p.Conference paper, (Refereed)
Abstract [en]

n this paper, we present a sliding-window variable-regularization recursive least squares algorithm. In contrast to standard recursive least squares, the algorithm presented in this paper operates on a finite window of data, where old data are discarded as new data become available. This property can be beneficial for estimating time-varying parameters. Furthermore, standard recursive least squares uses time-invariant regularization. More specifically, the inverse of the initial covariance matrix in standard recursive least squares can be viewed as a regularization term, which weights the difference between the next estimate and the initial estimate. This regularization is fixed for all steps of the recursion. The algorithm derived in this paper allows for time-varying regularization. In particular, the present paper allows for time varying regularization in the weighting as well as what is being weighted. Specifically, the regularization term can weight the difference between the next estimate and a time-varying vector of parameters rather than the initial estimate.

Place, publisher, year, edition, pages
San Francisco: IEEE conference proceedings, 2011. 3275-3280 p.
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Electrical Engineering
Identifiers
URN: urn:nbn:se:kau:diva-10747ISBN: 978-1-4577-0080-4 (print)OAI: oai:DiVA.org:kau-10747DiVA: diva2:494299
Conference
Proc. American Control Conf., San Francisco, CA, June 29 2011-July 1 2011
Available from: 2012-02-08 Created: 2012-02-08 Last updated: 2014-08-12Bibliographically approved

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