Discrete-to-continuous dynamics reconstruction for bilinear systems

P. Rumschinski, Dina Shona Laila, R. Findeisen

    Research output: Chapter in Book/Report/Conference proceedingConference proceedingpeer-review

    1 Citation (Scopus)


    In this work, we study the reconstruction of continuous-time models from discrete-time models of bilinear systems. Many dynamical systems contain nonlinearities and evolve continuously in time. However, due to the use of digital sensors for data acquisition, system identification methods typically rely on sampled data and yield a discrete-time model. Linking such a discrete-time model to its equivalent continuous-time dynamics is nontrivial. This paper proposes a discrete-to-continuous dynamics reconstruction for discrete-time models of a class of bilinear systems obtained by system identification. We show that for bilinear systems we can obtain a discrete-time model that is consistent with the measurement data. Furthermore, we show that one can derive from the discrete-time model, its equivalent continuous-time model. Two examples illustrate the approach.
    Original languageEnglish
    Title of host publication2012 IEEE 51st IEEE Conference on Decision and Control (CDC)
    ISBN (Electronic)978-1-4673-2066-5
    ISBN (Print)978-1-4673-2065-8
    Publication statusPublished - 4 Feb 2013
    EventIEEE Conference on Decision and Control - Hawaii, Maui, United States
    Duration: 10 Dec 201213 Dec 2012


    ConferenceIEEE Conference on Decision and Control
    Country/TerritoryUnited States
    City Maui

    Bibliographical note

    The full text is currently unavailable on the repository.


    • Nonlinear systems
    • Parameter estimation
    • Data models
    • Linear systems
    • Numerical models
    • Vectors
    • Time measurement
    • sampled data systems
    • bilinear systems
    • continuous time systems
    • data acquisition
    • discrete time systems
    • identification
    • equivalent continuous-time model
    • dynamical systems
    • nonlinearities
    • digital sensors
    • system identification methods
    • sampled data
    • discrete-time models
    • discrete-to-continuous dynamics reconstruction
    • measurement data
    • Bilinear system
    • Nonlinear identification
    • Discrete-to-continuous reconstruction


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