The Combined Methodology for Reducing the High Model Order for a Continuous System Using Genetic Algorithm and Dominant Poles Methods

Abstract

Model order reduction is one of the crucial topics facing researchers nowadays. Various methods were conducted for achieving this goal. In this article, genetic algorithm (GA) with dominant poles methods are used to reduce high-order transfer functions (TFs) to lower-order ones. Genetic algorithm is powerful technique used for optimization purposes. In this approach, genetic algorithm is applied to model order reduction to reduce the order of the numerator of TF whereas the dominant poles method is used to reduce the order of denominator of the TF and thus improving accuracy and preserving the same dominant poles for the reduced system as the original model which are two important issues for improving the performance of simulation and computation and maintaining the behavior of the original high order models being reduced