Generation of optimal linear parametric models for LFT-based robust stability analysis and control design

Harald Pfifer, Simon Hecker

Research output: Chapter in Book/Report/Conference proceedingConference contribution

19 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationProceedings of the 47th IEEE Conference on Decision and Control, CDC 2008
Pages3866-3871
Number of pages6
DOIs
StatePublished - Dec 1 2008
Event47th IEEE Conference on Decision and Control, CDC 2008 - Cancun, Mexico
Duration: Dec 9 2008Dec 11 2008

Publication series

NameProceedings of the IEEE Conference on Decision and Control
ISSN (Print)0191-2216

Other

Other47th IEEE Conference on Decision and Control, CDC 2008
Country/TerritoryMexico
CityCancun
Period12/9/0812/11/08

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