On Model Error Modeling in Set Membership Identification
نویسندگان
چکیده
A recent perspective on model error modeling is applied to set membership identification techniques in order to highlight the separation between unmodeled dynamics and noise. Model validation issues are also easily addressed in the proposed framework. The computation of the minimum noise bound for which a nominal model is not falsified by i/o data, can be used as a rationale for selecting an appropriate model class. Uncertainty is evaluated in terms of the frequency response, so that it can be handled by H∞ control techniques.
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