Estimation of Bounded Model Uncertainties
Olivier Adrot*, Jean-Marie Flaus*, and José Ragot**
*Laboratoire d’Automatique de Grenoble - UMR 5528, ENSIEG, BP 46, 38402 St Martin d’Hères Cedex, France
**Centre de Recherche en Automatique de Nancy - UMR 7039, ENSEM, 2, avenue de la Forêt de Haye, 54516 Vandœuvre-lès-Nancy Cedex, France
We identify parameters of a given input-output model so that estimated model output is consistent with the measured output of the system modeled. Parameter estimation based on a set-membership approach is a nonprobabilistic method for characterizing the uncertainty with which each model parameter is known. The model is consistent with data if the estimated output domain contains measured system output at each instant. Dynamic linear Multi-Input Multi-Output (MIMO) models are considered in this paper. Every equation error is bounded while model parameters fluctuate within a time-invariant domain represented by a zonotope. Our proposal helps find the characteristics of this domain, e.g., center, shape, size, by taking into account coupling between bounded variables of output equations to increase model accuracy.
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