Abstract
Abstract We present a novel approach to the selection of dynamic system models with regard to stabilisability in terms of their observer error dynamics. For that, we introduce the gap metric for the observer error dynamics as a distance measure. Models are selected for different subsets in such a way, that the gap metric is minimised. The procedure is subsequently applied to classify parametrised Gottingen Minipig models, which were obtained from animal experimental data, into different subsets. These obtained classes are then set up as the basis for an optimal experimental design procedure and result in improved convergence properties, as well as, in reduced implementation effort.
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