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Parametric Identification of Linear Systems Followed by Non-Invertible Piecewise Nonlinearities
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Parametric Identification of Linear Systems Followed by Non-Invertible Piecewise Nonlinearities

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Abstract:

The aim of the given paper is the development of an approach for parametric identification of Wiener systems with static non-invertible function, i.e., when the linear part with unknown parameters is followed by piecewise linear nonlinearity with negative slopes. It is shown here that the problem of identification of a nonlinear Wiener system could be reduced to a linear parametric estimation problem by a simple input-output data reordering and by a following data partition into three data sets. A technique based on ordinary least squares (LS) is proposed here for the separate estimation of parameters of linear and nonlinear parts of the Wiener system, including the unknown threshold of piecewise nonlinearity, by processing respective particles of input-output observations. The simulation results are given.

Info:

Periodical:
International Frontier Science Letters (Volume 6)
Pages:
16-27
DOI:
https://doi.org/10.18052/www.scipress.com/IFSL.6.16
Citation:
R. Pupeikis, "Parametric Identification of Linear Systems Followed by Non-Invertible Piecewise Nonlinearities", International Frontier Science Letters, Vol. 6, pp. 16-27, 2015
Online since:
December 2015
Authors:
Rimantas Pupeikis
Keywords:
Missing Observations, Non-Invertible Nonlinearity, Nonlinear Systems, Parametric Identification, Wiener System
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Distribution:
Open Access

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References:

[1] G. Tao, P.V. Kokotovic, Adaptive Control of Systems with Actuator and Sensor Nonlinearities, Wiley: New York, (1996).

[2] E.W. Bai, V. Cerone, D. Regruto, Separable inputs for the identification of block-oriented nonlinear systems, Proc. of the 2007 American Control Conf., New York, pp.1548-1553, July (2007).

DOI: https://doi.org/10.1109/acc.2007.4282538

[3] J. Vörös, Compound operator decomposition and its application to Hammerstein and Wiener systems, in: F. Giri, E.W. Bai (Eds. ), Block-oriented Nonlinear Systems, in: Lecture Notes in Control and Information Sciences, Springer, Heidelberg, 404, 2010, 35-51.

DOI: https://doi.org/10.1007/978-1-84996-513-2_4

[4] L. Ljung, System Identification, Prentice-Hall PTR: New Jersey, (1999).

[5] G. Tao, P.V. Kokotovic, Adaptive control of plants with unknown dead-zones, IEEE Transactions on Automatic Control. 39(1), 1994, 59-68.

DOI: https://doi.org/10.1109/9.273339

[6] J. Vörös, Recursive identification of systems with noninvertible output nonlinearities, Informatica, 21(1), 2010, 139-148.

[7] A. Hagenblad, Aspects of the Identification of Wiener Models, Linköping studies in science and technology, Thesis No 793, Division of Automatic Control, Department of Electrical Engineering, Linköpings Universitet, SE-581 83 Linköping, Sweden, (1999).

[8] R. Pupeikis, On the identification of Wiener systems having saturation-like functions with positive slopes, Informatica, 16(1), 2005, 131-144.

[9] K. Kazlauskas, R. Pupeikis, Self-tuning control of linear systems followed by dead-zones, TEM journal, 3(1), February 2014, 3-7.

[10] R. Pupeikis, Self-tuning minimum variance control of linear systems followed by saturation nonlinearities in a noisy frame, Int. Journal of Robust and Nonlinear Control. 24(2), January 2014, 313-325.

DOI: https://doi.org/10.1002/rnc.2888

[11] J. S. Bendat, A.G. Piersol, Measurement and Analysis of Random Data, Wiley: New York, (1967).

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