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Identification of a Class of Nonlinear Dynamical Networks⁎
KTH, School of Electrical Engineering and Computer Science (EECS), Automatic Control.ORCID iD: 0000-0001-5474-7060
KTH, School of Electrical Engineering and Computer Science (EECS), Automatic Control.ORCID iD: 0000-0003-0355-2663
KTH, School of Electrical Engineering and Computer Science (EECS), Automatic Control.ORCID iD: 0000-0002-9368-3079
2018 (English)In: IFAC-PapersOnLine, E-ISSN 2405-8963, Vol. 51, no 15, p. 868-873Article in journal (Refereed) Published
Abstract [en]

Identification of dynamic networks has attracted considerable interest recently. So far the main focus has been on linear time-invariant networks. Meanwhile, most real-life systems exhibit nonlinear behaviors; consider, for example, two stochastic linear time-invariant systems connected in series, each of which has a nonlinearity at its output. The estimation problem in this case is recognized to be challenging, due to the analytical intractability of both the likelihood function and the optimal one-step ahead predictors of the measured nodes. In this contribution, we introduce a relatively simple prediction error method that may be used for the estimation of nonlinear dynamical networks. The estimator is defined using a deterministic predictor that is nonlinear in the known signals. The estimation problem can be defined using closed-form analytical expressions in several non-trivial cases, and Monte Carlo approximations are not necessarily required. We show, that this is the case for some block-oriented networks with no feedback loops and where all the nonlinear modules are polynomials. Consequently, the proposed method can be applied in situations considered challenging by current approaches. The performance of the estimation method is illustrated on a numerical simulation example.

Place, publisher, year, edition, pages
Elsevier B.V. , 2018. Vol. 51, no 15, p. 868-873
Keywords [en]
Block-Oriented Models, Dynamical Networks, Prediction Error Method, Stochastic Systems, System Identification, Estimation, Identification (control systems), Invariance, Linear systems, Numerical methods, Real time systems, Stochastic models, Time varying control systems, Analytical expressions, Block oriented model, Likelihood functions, Linear time invariant networks, Linear time invariant systems, Monte-carlo approximations, Nonlinear analysis
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:kth:diva-247495DOI: 10.1016/j.ifacol.2018.09.113ISI: 000446599200147Scopus ID: 2-s2.0-85054394061OAI: oai:DiVA.org:kth-247495DiVA, id: diva2:1301984
Note

QC 20190403

Available from: 2019-04-03 Created: 2019-04-03 Last updated: 2019-05-20Bibliographically approved

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Abdalmoaty, Mohamed R.Rojas, Cristian R.Hjalmarsson, Håkan

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