Optimal approximants for MIMO model reduction systems using genetic algorithms

Several analytical models reduction techniques have been proposed in literature to reduce complexity relating to high dimensionality of mathematical models representing physical systems. Genetic algorithm (GA) has proved to be an excellent optimisation tool in the past few years. Throughout this work, we built three different algorithms namely stability equation, Mihailov criterion, and the modified pole clustering techniques, which solve the multivariable model reduction problems and permit to obtain globally optimised nominal models. The aim of this paper is to highlight the efficiency and the performance of these tools over the existing conventional computing techniques.