A novel reduced order modeling of interval system using soft computing optimization approach

Abstract

This research article presents a novel algorithm for the model order reduction of higher order linear time interval systems using soft computing optimization approach. In the proposed method, a new recursive formula for alpha parameters is developed for determining reduced order interval model without formulating alpha and beta tables. The denominator and numerator polynomials of reduced order interval model are determined based on minimization of a multi-objective function comprising integral squared error and impulse response energy error using particle swarm optimization technique. The proposed algorithm has several advantageous features such as reduced computational complexity and stability preservation property. The efficacy of the proposed algorithm is illustrated through typical numerical examples available in the literature, and the results are successfully compared with the other familiar methods.