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Nonparametric Techniques in System Identification: The Time-Varying and Missing Data Cases

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Encyclopedia of Systems and Control
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Abstract

This entry is an extension of the nonparametric techniques presented in ā€œNonparametric Techniques in System Identificationā€, by Rik Pintelon and Johan Schoukens, to time- and parameter-varying systems and missing data problems. To increase its readability, we first briefly recall in the introduction some definitions given in ā€œNonparametric Techniques in System Identificationā€, by Rik Pintelon and Johan Schoukens, and clarify the leakage contribution to nonparametric frequency response function (FRF) estimation. Given its importance in the nonparametric estimation of time- and parameter-varying dynamics, the entry starts by describing the newest developments in advanced techniques for estimating FRFs. Next, the time-varying, parameter-varying, and missing data cases are handled.

As a main result the reader will learn about (i) the detection and quantification of time-varying effects and nonlinear distortions in FRF estimates, (ii) the estimation of time- and parameter-varying FRFs, and (iii) the estimation of (time-varying) FRFs in the presence of missing data. All results are valid for discrete- and continuous-time systems operating in open or closed loop.

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Acknowledgements

This work is sponsored in part by the Research Foundation Flanders (FWO-Vlaanderen) and in part by the Flemisch Government (Methusalem Fund, METH1).

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Correspondence to R. Pintelon .

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Pintelon, R., Lataire, J. (2019). Nonparametric Techniques in System Identification: The Time-Varying and Missing Data Cases. In: Baillieul, J., Samad, T. (eds) Encyclopedia of Systems and Control. Springer, London. https://doi.org/10.1007/978-1-4471-5102-9_100164-1

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  • DOI: https://doi.org/10.1007/978-1-4471-5102-9_100164-1

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