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On the Input Design for Kernel-based Regularized LTI System Identification: Power-constrained Inputs
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
Chinese Univ Hong Kong, Peoples R China.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
2017 (English)In: 2017 IEEE 56TH ANNUAL CONFERENCE ON DECISION AND CONTROL (CDC), IEEE , 2017Conference paper, Published paper (Refereed)
Abstract [en]

This paper considers the input design of kernelbased regularization methods for LTI system identification. We first derive the Bayesian mean squared error matrix under the Bayesian perspective, and then use some typical scalar measures (e.g., the A-optimality, D-optimality, and E-optimality) as optimization criteria for the input design problem. Instead of directly solving the nonconvex optimization problem, we propose a two-step procedure. The first step is to solve a convex optimization and the second one is to determine the inverse image of a quadratic map. Both of these two steps can be solved efficiently by the proposed method and hence all the globally optimal inputs are found. In particular, we show that for some kernels, the optimal input under the D-optimality has an explicit expression.

Place, publisher, year, edition, pages
IEEE , 2017.
Series
IEEE Conference on Decision and Control, ISSN 0743-1546
Keywords [en]
Input design; Bayesian mean squared error matrix; kernel-based regularization method; system identification; convex optimization; quadratic map
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:liu:diva-145489DOI: 10.1109/CDC.2017.8264437ISI: 000424696905008ISBN: 978-1-5090-2873-3 (print)OAI: oai:DiVA.org:liu-145489DiVA, id: diva2:1187188
Conference
IEEE 56th Annual Conference on Decision and Control (CDC)
Note

Funding Agencies|Thousand Youth Talents Plan - central government of China; Shenzhen Projects - Shenzhen Science and Technology Innovation Council [Ji-20170189, Ji-20160207]; Chinese University of Hong Kong, Shenzhen [PF. 01.000249, 2014.0003.23]; Swedish Research Council [2014-5894]; NSFC [61773329]

Available from: 2018-03-02 Created: 2018-03-02 Last updated: 2018-03-02

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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf