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A nonparametric kernel-based approach to Hammerstein system identification
KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.ORCID iD: 0000-0002-9368-3079
2017 (English)In: Automatica, ISSN 0005-1098, E-ISSN 1873-2836, Vol. 85, 234-247 p.Article in journal (Refereed) Published
Abstract [en]

Hammerstein systems are the series composition of a static nonlinear function and a linear dynamic system, In this work, we propose a nonparametric method for the identification of Hammerstein systems. We adopt a kernel-based approach to model the two components of the system. In particular, we model the nonlinear function and the impulse response of the linear block as Gaussian processes with suitable kernels. The kernels can be chosen to encode prior information about the nonlinear function and the system. Following the empirical Bayes approach, we estimate the posterior mean of the impulse response using estimates of the nonlinear function, of the hyperparameters, and of the noise variance. These estimates are found by maximizing the marginal likelihood of the data. This maximization problem is solved using an iterative scheme based on the expectation-conditional maximization, which is a variation of the standard expectation maximization method for solving maximum-likelihood problems. We show the effectiveness of the proposed identification scheme in some simulation experiments.

Place, publisher, year, edition, pages
2017. Vol. 85, 234-247 p.
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:kth:diva-218227DOI: 10.1016/j.automatica.2017.07.055ISI: 000414818100027Scopus ID: 2-s2.0-85027880897OAI: oai:DiVA.org:kth-218227DiVA: diva2:1160819
Note

QC 20171128

Available from: 2017-11-28 Created: 2017-11-28 Last updated: 2017-11-28Bibliographically approved

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Hjalmarsson, Håkan

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