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Particle-based Gaussian process optimization for input design in nonlinear dynamical models
KTH Royal Inst Technol, Sch Elect Engn, Dept Automat Control, SE-10044 Stockholm, Sweden.;KTH Royal Inst Technol, Sch Elect Engn, ACCESS Linnaeus Ctr, SE-10044 Stockholm, Sweden..
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology.
KTH Royal Inst Technol, Sch Elect Engn, Dept Automat Control, SE-10044 Stockholm, Sweden.;KTH Royal Inst Technol, Sch Elect Engn, ACCESS Linnaeus Ctr, SE-10044 Stockholm, Sweden..
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.ORCID iD: 0000-0001-5183-234X
2016 (English)In: 2016 IEEE 55th Conference On Decision And Control (CDC), New York: IEEE, 2016, 2085-2090 p.Conference paper, Published paper (Refereed)
Abstract [en]

We propose a novel approach to input design for identification of nonlinear state space models. The optimal input sequence is obtained by maximizing a scalar cost function of the Fisher information matrix. Since the Fisher information matrix is unavailable in closed form, it is estimated using particle methods. In addition, we make use of Gaussian process optimization to find the optimal input and to mitigate the problem of a large computational cost incurred by the particle method, as the method reduces the number of functional evaluations. Numerical examples are provided to illustrate the performance of the resulting algorithm.

Place, publisher, year, edition, pages
New York: IEEE, 2016. 2085-2090 p.
Series
IEEE Conference on Decision and Control, ISSN 0743-1546
Keyword [en]
System identification, input design, Gaussian process optimization
National Category
Robotics
Identifiers
URN: urn:nbn:se:uu:diva-331392DOI: 10.1109/CDC.2016.7798571ISI: 000400048102043ISBN: 978-1-5090-1837-6 (electronic)OAI: oai:DiVA.org:uu-331392DiVA: diva2:1149425
Conference
55th IEEE Conference on Decision and Control (CDC), Dec 12-14, 2016, Las Vegas, NV
Funder
Swedish Research Council, 621-2013-5524, 621-2009-4017
Available from: 2017-10-16 Created: 2017-10-16 Last updated: 2017-10-16Bibliographically approved

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Schön, Thomas B.

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