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Identifying MIMO Hammerstein systems in the context of subspace model identification methods. (English) Zbl 0848.93014

The paper deals with identification of nonlinear MIMO Hammerstein systems by applying the family of subspace model identification methods, developed by one of the co-authors in previous papers. Identification of both the linear dynamic part and the static nonlinearity from input-output data of the whole system is discussed assuming generally a polynomial natural for the nonlinearity. Two kinds of a priori information about the nonlinear part of the system are assumed. The first kind assumes detailed knowledge of the particular polynomial structure of the nonlinearity; then the unknown coefficients in the respective polynomial parametrization are to be identified. The second kind assumes that only partial, qualitative, information on the structure of the nonlinearity is available, regarding e.g. the presence of dominant odd or even terms in the description, and the ‘approximation’ of the true map by a polynomial of finite order has to be performed. For these situations, the algorithms leading to consistent estimates of the linear time invariant part and nonlinear part of the system are presented and discussed for different particular cases of nonlinearity. Numerical illustrative examples are included.

MSC:

93B30 System identification
93C10 Nonlinear systems in control theory

Software:

Matlab
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References:

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