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Closed-loop Identification of MIMO Systems in the Prediction Error Framework: Data Informativity Analysis

Authors
  • Colin, Kévin
  • Bombois, Xavier
  • Bako, Laurent
  • Morelli, Federico
Publication Date
Nov 06, 2019
Source
Kaleidoscope Open Archive
Keywords
Language
English
License
Unknown
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Abstract

In the Prediction Error Identification framework, it is essential that the experiment yields informative data with respect to the chosen model structure to get a consistent estimate. In this work, we focus on the data informativity property for the identification of Multi-Inputs Multi-Outputs system in closed-loop and we derive conditions to verify if a given external excitation combined with the feedback introduced by the controller yields informative data with respect to the model structure. This study covers the case of the classical model structures used in prediction-error identification and the classical types of external excitation vectors, i.e., vectors whose elements are either multisine or filtered white noises.

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