Abstract:
We present a general approach to generate a linear parametric state-space model, which approximates a nonlinear system with high accuracy. It is optimally suited for LFT-...Show MoreMetadata
Abstract:
We present a general approach to generate a linear parametric state-space model, which approximates a nonlinear system with high accuracy. It is optimally suited for LFT-based robust stability analysis and control design. At the beginning a Jacobian-based linearization is applied to generate a set of linearized state-space systems describing the local behavior of the nonlinear plant about the corresponding equilibrium points. These models are then approximated using multivariable polynomial fitting techniques in combination with global optimization. The objective is 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 a robust stability analysis for a nonlinear generic missile model.
Published in: 2008 47th IEEE Conference on Decision and Control
Date of Conference: 09-11 December 2008
Date Added to IEEE Xplore: 06 January 2009
ISBN Information:
Print ISSN: 0191-2216