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Communication Dans Un Congrès Année : 2020

Data Informativity for the Identification of particular Parallel Hammerstein Systems

Résumé

To obtain a consistent estimate when performing an identication with Prediction Error, it is important that the excitation yields informative data with respect to the chosen model structure. While the characterization of this property seems to be a mature research area in the linear case, the same cannot be said for nonlinear systems. In this work, we study the data informativity for a particular type of Hammerstein systems for two commonly-used excitations: white Gaussian noise and multisine. The real life example of the MEMS gyroscope is considered.
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Dates et versions

hal-02392113 , version 1 (03-12-2019)

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Kévin Colin, Xavier Bombois, Laurent Bako, Federico Morelli. Data Informativity for the Identification of particular Parallel Hammerstein Systems. 21th IFAC World Congress, Jul 2020, Berlin, Germany. pp.1102-1107, ⟨10.1016/j.ifacol.2020.12.1308⟩. ⟨hal-02392113⟩
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