Generation of LPV and LFT-models for the COFCLUO nonlinear aircraft model

Abstract

We present a general approach to generate a linear parametric state-space model, which approximates a nonlinear sytem with high accuracy and is optimally suited for Linear Fractional Transformation (LFT)-based robust stability analysis and control design. As a starting point a set of linearized state space systems is generated describing the local behaviour of the nonlinear plant about the corresponding equilibrium points. These models are then approximated using multivariable polynomial fitting techniques in combination with global optimization, with the objective to find a linear parametric model, which allows the transformation into a Linear Fractional Representation (LFR) of least possible order. A gap metric constraint is included during the optimization in order to guarantee a specified accuracy of the transfer function of the linear parametric model. The effectiveness of the proposed method is demonstrated by the generation of highly accurate LFT-models covering large regions of the flight envelope of the COFCLUO nonlinear benchmark model.