Identifying MIMO Hammerstein systems in the context of subspace model identification methods

Abstract In this paper, we outline the extension of the MOESP (standing for Multivariable Output Error State sPace model identification and introduced in [1].) family of subspace model identification schemes to Hammerstein type of non-linear systems. One type of identification problem is considered. This type addresses the identification of both the linear dynamic part and the static nonlinearity, where only limited a priori information regarding the structure of the nonlinearity is available. Another (c) type of Hammerstein identification problem, considered in [2], assumes the (polynomial) structure of the static non-linearity to be given and the task here is to identify similarly the linear system dynamics and the unknown proportional constants in the parametrization of the static non-linearity. The improved robustness properties of the algorithms developed in this paper over existing correlation based schemes is illustrated in [2].

[1]  T. Söderström,et al.  Instrumental-variable methods for identification of Hammerstein systems , 1982 .

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

[3]  M. Verhaegen,et al.  Identifying MIMO Hammerstein systems in the context of subspace model identification methods , 1996 .

[4]  M. Verhaegen.  Subspace model identification Part 2. Analysis of the elementary output-error state-space model identification algorithm , 1992 .

[5]  Heinz Unbehauen,et al.  Structure identification of nonlinear dynamic systems - A survey on input/output approaches , 1990, Autom..

[6]  W. Greblicki,et al.  Identification of discrete Hammerstein systems using kernel regression estimates , 1986 .

[7]  M. Pawlak.  On the series expansion approach to the identification of Hammerstein systems , 1991 .

[8]  L. Zi-qiang.  Controller design oriented model identification method for Hammerstein system , 1993 .

[9]  Michel Verhaegen.  A Subspace Model Identification Solution to the Identification of Mixed Causal, Anti-Causal LTI Systems , 1996, SIAM J. Matrix Anal. Appl..

[10]  Patrick Dewilde,et al.  Subspace model identification Part 1. The output-error state-space model identification class of algorithms , 1992 .

[11]  Michel Verhaegen,et al.  Application of a subspace model identification technique to identify LTI systems operating in closed-loop , 1993, Autom..

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

[13]  R. W. Miksad,et al.  Application of digital cross-bispectral analysis techniques to model the non-linear response of a moored vessel system in random seas , 1985 .