Linear fractional representations (LFRs) 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 LFRs, 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 chapter a new approach to LFR modelling and identification starting from equation-based, object-oriented descriptions of the plant dynamics (formulated using the Modelica language) and input-output data is 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 LFR-based identification and control techniques.

Integrated modelling and parameter estimation: an LFR/Modelica approach

LOVERA, MARCO;CASELLA, FRANCESCO
2014

Abstract

Linear fractional representations (LFRs) 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 LFRs, 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 chapter a new approach to LFR modelling and identification starting from equation-based, object-oriented descriptions of the plant dynamics (formulated using the Modelica language) and input-output data is 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 LFR-based identification and control techniques.
Control-oriented Modelling and Identification: Theory and practice
9781849196147
Control engineering computing; Differential algebraic equations; Differential-algebraic model; Formal languages; Formal specification; Integrated modelling; LFR; Linear fractional representation; Model description formalism; Modelica language; Nonlinear control systems; Nonlinear plant dynamics; Object diagram; Object-oriented description; Object-oriented programming; Parameter estimation; Parameter estimation
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/908559
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