A Unified Approach for the Identification of SISO/MIMO Wiener and Hammerstein Systems

Abstract Hammerstein and Wiener models are nonlinear representations of systems composed by the coupling of a static nonlinearity N and a linear system L in the form N-L and L-N respectively. These models can represent real processes which made them popular in the last decades. The problem of identifying the static nonlinearity and linear system is not a trivial task, and has attracted a lot of research interest. It has been studied in the available literature either for Hammerstein or Wiener systems, and either in a discrete-time or continuous-time setting. The objective of this paper is to present a unified framework for the identification of these systems that is valid for SISO and MIMO systems, discrete and continuous-time setting, and with the only a priori knowledge that the system is either Wiener or Hammerstein.

[1]  Lars-Henning Zetterberg,et al.  Identification of certain time-varying nonlinear Wiener and Hammerstein systems , 2001, IEEE Trans. Signal Process..

[2]  Heinz Unbehauen,et al.  Continuous-time approaches to system identification - A survey , 1990, Autom..

[3]  Lennart Ljung,et al.  System Identification: Theory for the User , 1987 .

[4]  Yucai Zhu,et al.  Multivariable System Identification For Process Control , 2001 .

[5]  Han-Fu Chen,et al.  Recursive identification for Wiener model with discontinuous piece-wise linear function , 2006, IEEE Transactions on Automatic Control.

[6]  Vito Cerone,et al.  Parameter bounds for discrete-time Hammerstein models with bounded output errors , 2003, IEEE Trans. Autom. Control..

[7]  Michel Verhaegen,et al.  Identification of the deterministic part of MIMO state space models given in innovations form from input-output data , 1994, Autom..

[8]  David T. Westwick,et al.  Identification of Hammerstein models with cubic spline nonlinearities , 2004, IEEE Transactions on Biomedical Engineering.

[9]  E. Bai,et al.  Block Oriented Nonlinear System Identification , 2010 .

[10]  Er-Wei Bai,et al.  Frequency domain identification of Wiener models , 2003, at - Automatisierungstechnik.

[11]  K. Narendra,et al.  An iterative method for the identification of nonlinear systems using a Hammerstein model , 1966 .

[12]  Stanko Strmcnik,et al.  Identification of nonlinear systems using a piecewise-linear Hammerstein model , 2005, Syst. Control. Lett..

[13]  Jie Bao,et al.  Identification of MIMO Hammerstein systems using cardinal spline functions , 2006 .

[14]  S. Sung System Identification Method for Hammerstein Processes , 2002 .

[15]  José Luis Figueroa,et al.  Wiener and Hammerstein uncertain models identification , 2009, Math. Comput. Simul..

[16]  Dietmar Bauer,et al.  Asymptotic properties of subspace estimators , 2005, Autom..

[17]  Wlodzimierz Greblicki Nonlinearity recovering in Wiener system driven with correlated signal , 2004, IEEE Transactions on Automatic Control.

[18]  Fouad Giri,et al.  An Analytic Geometry Approach to Wiener System Frequency Identification , 2009, IEEE Transactions on Automatic Control.

[19]  W. Rudin Real and complex analysis , 1968 .

[20]  Sirish L. Shah,et al.  Identification of Hammerstein models using multivariate statistical tools , 1995 .

[21]  Fouad Giri,et al.  Parameter identification of Hammerstein systems containing backlash operators with arbitrary-shape parametric borders , 2011, Autom..