» Download Article

Authors
Ni, Boyi; Garnier, Hugues; Gilson, Marion

Digital Object Identifier (DOI)
10.3182/20120711-3-BE-2027.00252

Page Numbers:
1061-1066

Index Terms
Nonlinear System Identification; Continuous Time System Estimation

Abstract
The continuous-time model identification problem for Hammerstein-Wiener systems with output measurement noise is studied. A simplified refined instrumental variable continuous-time (SRIVC) identification method is adopted. With the assumption of monotonic nonlinear function, the nonlinear model is iteratively estimated as an over-parameterized multiple-input single-output (MISO) linear time invariant model. Monte Carlo simulation analysis is presented to illustrate the effectiveness of the proposed method.

References

REFERENCES

E.W. Bai. A blind approach to the Hammerstein-Wiener model
identification. Automatica, 38(6):967–979, 2002.

E.W. Bai. An optimal two-stage identification algorithm for a class of
nonlinear systems. Automatica, 34:333– 338, 1998.

P. Crama and J. Schoukens. Hammerstein-Wiener system estimator
initialization. Automatica, 40:1543–1550, 2004.

A.H. Falkner. Iterative technique in the identification of a non-linear
system. International Journal of Control, 48 (1):385–396, 1988.

H. Garnier and L. Wang (Eds.) Identification of continuous-time models
from sampled data. SpringerVerlag, London, 2008b.

H. Garnier, M. Gilson, P.C. Young, and E. Huselstein. An optimal IV
technique for identifying continuoustime transfer function model of
multiple input systems. Control Engineering Practice, 15:471–486,
2007.

F. Giri and E.W. Bai (Eds.) Block-oriented Nonlinear System
Identification, volume 404 of Lecture Notes in Control and Information
Sciences. Springer, New York, 2010.

J.C. Gómez and E. Baeyens. Subspace identification of o multivariable
Hammerstein and Wiener models. In 15th Triennial World Congress,
55–60, Barcelona, Spain, 2002.

W. Greblicki. Nonparametric identification of Wiener systems by
orthogonal series. IEEE Transactions on Automatic Control,
39(10):2077–2086, 1994.

A.D. Kalafatis, L. Wang, and W.R. Cluett. Identification of Wiener-type
nonlinear systems in a noisy environment. International Journal of
Control, 66:923–941, 1997.

V. Laurain, M. Gilson, H. Garnier, and P.C. Young. Refined instrumental
variable methods for identification of Hammerstein continuous-time
Box-Jenkins models, In The 47th IEEE Conference on Decision and
Control (CDC’2008), 1386–1391, Cancun, Mexico, 2008.

V. Laurain, M. Gilson, R. T´th, and H. Garnier. Refined o instrumental
variable methods for identification of LPV Box-Jenkins models.
Automatica, 46(6):959–967, 2010.

O. Nelles. Nonlinear System Identification. Springer, Berlin, 2001.

J. Vörös. An iterative method for Hammerstein-Wiener systems parameter
identification. Journal of Electrical Engineering, 55(11-12):328–331,
2004.

J. Wang, T. Chen, and L. Wang. A blind approach to identification of
Hammerstein-Wiener systems corrupted by nonlinear-process noise. In
Proceedings of the 7th Asian Control Conference, 1340–1345, Hong Kong,
China, 2009a.

J. Wang, Q. Zhang, and L. Ljung. Revisiting Hammerstein system
identification through the two-stage algorithm for bilinear parameter
estimation. Automatica, 45(11): 2627–2633, 2009b.

P.C. Young, H. Garnier and L. Wang. Refined Instrumental Variable
Identification of Continuous-time Hybrid Box-Jenkins Models, In
Identification of Continous-time Models from Sampled Data, H. Garnier
and L. Wang. Eds. chapter 4. Springer, London, 2008.

P.C. Young. Recursive Estimation and Time-Series Analysis: An
Introduction for the Student and Practitioner. Springer, 2011.

Q. Zhang, A. Iouditski, and L. Ljung. Identification of Wiener systems
with monotonic nonlinearity. In 14th IFAC Symposium on System
Identification, 166–171, Newcastle, Australia, 2006.