Adaptive unknown input reconstruction scheme for Hammerstein-Wiener systems

M. Sumislawska, Keith J. Burnham

    Research output: Chapter in Book/Report/Conference proceedingChapter

    1 Citation (Scopus)
    31 Downloads (Pure)


    In this paper an adaptive time-varying filter for unknown/unmeasurable input reconstruction is proposed. The algorithm is based on parity-equations and is applicable to Hammerstein-Wiener systems, i.e. systems composed of a linear dynamic part followed and preceded by a memoryless nonlinearity. An error-in-variables case is considered, i.e. known input and output signals are both subjected to measurement uncertainties. The scheme forms an extension to a filter previously proposed by the authors. As the input reconstruction involves transformation of noisy signals through memoryless static functions, measurement noise is either amplified or reduced, depending on the gradient of the nonlinear function. Thus, in the proposed scheme the bandwidth of the filter is adjusted depending on the operating point allowing for a trade-off between noise attenuation and a phase lag.
    Original languageEnglish
    Title of host publicationControl (CONTROL), 2014 UKACC International Conference on
    Publication statusPublished - 2014
    EventUKACC International Conference on Control - , Loughborough, United Kingdom
    Duration: 9 Jul 201411 Jul 2014


    ConferenceUKACC International Conference on Control
    Abbreviated titleCONTROL
    Country/TerritoryUnited Kingdom

    Bibliographical note

    It was given at the 10th UKACC International Conference on Control, CONTROL 2014; Loughborough UniversityLoughborough; United Kingdom; 9 July 2014 through 11 July 2014


    • Algorithms
    • Economic and social effects
    • Nonlinear equations
    • Uncertainty analysis
    • Error in variables
    • Hammerstein-Wiener systems
    • Input and outputs
    • Input reconstruction
    • Measurement uncertainty
    • Nonlinear functions
    • Time varying filters
    • Unknown input reconstruction


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