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Generation of signals with specified second-order properties for constrained systems
KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.ORCID iD: 0000-0002-9368-3079
2016 (English)In: International journal of adaptive control and signal processing (Print), ISSN 0890-6327, E-ISSN 1099-1115, Vol. 30, no 3, 456-472 p.Article in journal (Refereed) PublishedText
Abstract [en]

This contribution considers the problem of realizing an input signal with a desired autocorrelation sequence satisfying both input and output constraints for the system it is to be applied to. This is an important problem in system identification, firstly, because the quality and accuracy of the identified model are highly dependent on the excitation signal used during the experiment and secondly, because on real processes, it is often important to constrain the input and output of the process because of actuator saturation and safety considerations. The signal generation is formulated as a model predictive controller with probabilistic constraints to make the algorithm robust to model uncertainties and process noise. The corresponding optimization problem is then solved with tools from scenario-based stochastic optimization. To reduce the model uncertainties, the method is made adaptive where a new model of the system and its uncertainties are reidentified. The algorithm is successfully applied to a simulation example and in a practical experiment for the identification of a quadruple tank lab process.

Place, publisher, year, edition, pages
2016. Vol. 30, no 3, 456-472 p.
Keyword [en]
signal generation, excitation signals, input design, constrained systems, uncertain systems, stochastic optimization
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:kth:diva-185376DOI: 10.1002/acs.2586ISI: 000372359800003ScopusID: 2-s2.0-84959887129OAI: oai:DiVA.org:kth-185376DiVA: diva2:920563
Funder
EU, FP7, Seventh Framework Programme, 257059
Note

QC 20160418

Available from: 2016-04-18 Created: 2016-04-18 Last updated: 2016-04-18Bibliographically approved

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Larsson, Christian A.Hägg, PerHjalmarsson, Håkan
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