Abstract
Linear Fractional Transformations (LFTs) are a widely used modelling framework in modern control and identification. Deriving such models from physical first principles is a non-trivial and often error-prone process, if carried out manually. In this paper a new approach to reduced-order LFT modelling and identification starting from equation-based, object-oriented (O-O) descriptions of the plant (formulated using the Modelica language) and experimental data are presented. This approach allows to reduce the gap between user-friendly model representations, based on object diagrams with physical connections, block diagrams with signal connections, and generic differential-algebraic models, and the use of advanced LFT based identification and control techniques.
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