Identification of decoupled polynomial narx model using simulation error minimization

Kiana Karami, David Westwick, Johan Schoukens

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    1 Scopus citations

    Abstract

    The Polynomial Nonlinear Auto-Regressive eXogenous input (P-NARX) model, a multivariable polynomial of past input and output values, is a widely used equation error nonlinear system model. The number of model parameters grows rapidly with the polynomial degree, and with the number of past inputs and outputs, but can be reduced significantly by adopting a decoupled structure, consisting of a transformation matrix followed by a bank of single-input single-output polynomials whose outputs are summed to produce the final output. Prediction Error Minimization (PEM) is a classical approach for the identification of both linear and nonlinear systems. Models trained using PEM may not be suitable for system simulation, where the model only has access to the system's inputs. In this paper, an identification method based on Simulation Error Minimization (SEM) for Decoupled P-NARX models is proposed. The proposed algorithm is applied to data from two nonlinear system identification benchmarks and the performance is compared to a previous PEM based algorithm.

    Original languageEnglish (US)
    Title of host publication2019 American Control Conference, ACC 2019
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages4362-4367
    Number of pages6
    ISBN (Electronic)9781538679265
    StatePublished - Jul 2019
    Event2019 American Control Conference, ACC 2019 - Philadelphia, United States
    Duration: Jul 10 2019Jul 12 2019

    Publication series

    NameProceedings of the American Control Conference
    Volume2019-July
    ISSN (Print)0743-1619

    Conference

    Conference2019 American Control Conference, ACC 2019
    CountryUnited States
    CityPhiladelphia
    Period7/10/197/12/19

    All Science Journal Classification (ASJC) codes

    • Electrical and Electronic Engineering

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