Towards integrated modelling and parameter estimation: an LFT-Modelica approach

Abstract

Linear Fractional Transformations (LFTs) are a widely used model description formalism in modern control and system identification theory. Deriving such models from physical first principles is a non-trivial and often tedious and error-prone process, if carried out manually. Tools already exist to transform symbolic transfer functions and symbolic state-space representations into reduced-order LFTs, but these descriptions are still quite far from a natural, physical-based, object oriented description of physical and technological systems and are moreover hard to integrate with model identification tools. In this paper the results derived so far in the direction of developing a new approach to reduced-order LFT modelling and identification starting from equation-based, object-oriented descriptions of the plant dynamics (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 connection, and generic differential-algebraic models, and the use of advanced LFT based identification and control techniques.