Abstract
This entry is an extension of the nonparametric techniques presented in “Nonparametric Techniques in System Identification”, 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 “Nonparametric Techniques in System Identification”, 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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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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