liu.seSearch for publications in DiVA
Change search
Link to record
Permanent link

Direct link
BETA
Verhaegen, Michel
Publications (2 of 2) Show all publications
Hansson, A. & Verhaegen, M. (2014). Distributed system identification with ADMM. In: Proceedings of the 53rd IEEE Conference on Decision and Control: . Paper presented at 53rd IEEE Conference on Decision and Control 15-17 Dec. 2014, Los Angeles, CA (pp. 290-295). Los Angeles
Open this publication in new window or tab >>Distributed system identification with ADMM
2014 (English)In: Proceedings of the 53rd IEEE Conference on Decision and Control, Los Angeles, 2014, p. 290-295Conference paper, Published paper (Refereed)
Abstract [en]

This paper presents identification of both network connected systems as well as distributed systems governed by PDEs in the framework of distributed optimization via the Alternating Direction Method of Multipliers. This approach opens first the possibility to identify distributed models in a global manner using all available data sequences and second the possibility for a distributed implementation. The latter will make the application to large scale complex systems possible. In addition to outlining a new large scale identification method, illustrations are shown for identifying both network connected systems and discretized PDEs.

Place, publisher, year, edition, pages
Los Angeles: , 2014
Series
53rd IEEE Conference on Decision and Control, ISSN 0191-2216
National Category
Computer Sciences
Identifiers
urn:nbn:se:liu:diva-129292 (URN)10.1109/CDC.2014.7039396 (DOI)978-1-4799-7745-1 (ISBN)978-1-4673-6088-3 (ISBN)
Conference
53rd IEEE Conference on Decision and Control 15-17 Dec. 2014, Los Angeles, CA
Available from: 2016-06-15 Created: 2016-06-15 Last updated: 2018-01-10
Verhaegen, M. & Hansson, A. (2014). Nuclear norm subspace identification (N2SID) for short data batches. In: Proceedings of IFAC 2014 World Congress: . Paper presented at Preprints of the 19th World Congress The International Federation of Automatic Control. Cape Town, South Africa. August 24-29, 2014 (pp. 9528-9533). Cape Town
Open this publication in new window or tab >>Nuclear norm subspace identification (N2SID) for short data batches
2014 (English)In: Proceedings of IFAC 2014 World Congress, Cape Town, 2014, p. 9528-9533Conference paper, Published paper (Refereed)
Abstract [en]

Subspace identification is revisited in the scope of nuclear norm minimization methods. It is shown that essential structural knowledge about the unknown data matrices in the data equation that relates Hankel matrices constructed from input and output data can be used in the first step of the numerical solution presented. The structural knowledge comprises the low rank property of a matrix that is the product of the extended observability matrix and the state sequence and the Toeplitz structure of the matrix of Markov parameters (of the system in innovation form). The new subspace identification method is referred to as the N2SID (twice the N of Nuclear Norm and SID for Subspace IDentification) method. In addition to include key structural knowledge in the solution it integrates the subspace calculation with minimization of a classical prediction error cost function. The nuclear norm relaxation enables us to perform such integration while preserving convexity. The advantages of N2SID are demonstrated in a numerical open- and closed-loop simulation study. Here a comparison is made with another widely used SID method, i.e. N4SID. The comparison focusses on the identification with short data batches, i.e. where the number of measurements is a small multiple of the system order.

Place, publisher, year, edition, pages
Cape Town: , 2014
Keywords
Subspace system identification, Nuclear norm optimization, Rank constraint, Short
National Category
Control Engineering
Identifiers
urn:nbn:se:liu:diva-129293 (URN)10.3182/20140824-6-ZA-1003.00386 (DOI)
Conference
Preprints of the 19th World Congress The International Federation of Automatic Control. Cape Town, South Africa. August 24-29, 2014
Available from: 2016-06-15 Created: 2016-06-15 Last updated: 2016-06-29
Organisations

Search in DiVA

Show all publications