Enhanced LFR-toolbox for MATLAB

Abstract

We describe recent developments and enhancements of the LFR-toolbox for Matlab for building LFT-based uncertainty models and for LFT-based gain scheduling. A major development is the new LFT-object definition supporting a large class of uncertainty descriptions: continuous- and discrete-time uncertain models, regular and singular parametric expressions, more general uncertainty blocks (nonlinear, time-varying, etc.). By associating names to uncertainty blocks the reusability of generated LFT-representations and the user friendliness of manipulation of LFR-descriptions have been highly increased. Significant enhancements of the computational efficiency and of numerical accuracy have been achieved by employing efficient and numerically robust Fortran implementations of order reduction tools via mexfunction interfaces. The new enhancements in conjunction with improved symbolical preprocessing lead generally to a faster generation of LFT-representations with significantly lower orders.