Identification of parallel Wiener-Hammerstein systems with a decoupled static nonlinearity

Abstract Block-oriented models are often used to model a nonlinear system. This paper presents an identification method for parallel Wiener-Hammerstein systems, where the obtained model has a decoupled static nonlinear block. This decoupled nature makes the interpretation of the obtained model more easy. First a coupled parallel Wiener-Hammerstein model is estimated. Next, the static nonlinearity is decoupled using a tensor decomposition approach. Finally, the method is validated on real-world measurements using a custom built parallel Wiener-Hammerstein test system.

[1]  J. Chang,et al.  Analysis of individual differences in multidimensional scaling via an n-way generalization of “Eckart-Young” decomposition , 1970 .

[2]  Richard A. Harshman,et al.  Foundations of the PARAFAC procedure: Models and conditions for an "explanatory" multi-model factor analysis , 1970 .

[3]  Wilson J. Rugh,et al.  Complete identification of a class of nonlinear systems from steady state frequency response , 1974, CDC 1974.

[4]  W. Rugh,et al.  Further results on the identification problem for the class of nonlinear systems S_M , 1976 .

[5]  S. Billings,et al.  Identification of non-linear Sm systems , 1979 .

[6]  G. Palm On representation and approximation of nonlinear systems , 1978, Biological Cybernetics.

[7]  Gérard Favier,et al.  Parametric complexity reduction of Volterra models using tensor decompositions , 2009, 2009 17th European Signal Processing Conference.

[8]  Tamara G. Kolda,et al.  Tensor Decompositions and Applications , 2009, SIAM Rev..

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

[10]  Yves Rolain,et al.  Parametric Identification of Parallel Hammerstein Systems , 2011, IEEE Transactions on Instrumentation and Measurement.

[11]  Yves Rolain,et al.  Cross-term Elimination in Parallel Wiener Systems Using a Linear Input Transformation , 2012, IEEE Transactions on Instrumentation and Measurement.

[12]  Yves Rolain,et al.  Parametric Identification of Parallel Wiener Systems , 2012, IEEE Transactions on Instrumentation and Measurement.

[13]  Rik Pintelon,et al.  System Identification: A Frequency Domain Approach , 2012 .

[14]  Lieven De Lathauwer,et al.  Optimization-Based Algorithms for Tensor Decompositions: Canonical Polyadic Decomposition, Decomposition in Rank-(Lr, Lr, 1) Terms, and a New Generalization , 2013, SIAM J. Optim..

[15]  Yves Rolain,et al.  An identification algorithm for parallel Wiener-Hammerstein systems , 2013, 52nd IEEE Conference on Decision and Control.

[16]  Johan Schoukens,et al.  From coupled to decoupled polynomial representations in parallel Wiener-Hammerstein models , 2013, 52nd IEEE Conference on Decision and Control.