A Novel Model Order Reduction Technique for Linear Continuous-Time Systems Using PSO-DV Algorithm

Abstract

In this paper a new frequency-domain model order reduction method is proposed for the reduction of higher-order linear continuous-time single input single output systems using a recent hybrid evolutionary algorithm. The hybrid evolutionary algorithm is developed from the mutual synergism of particle swarm optimization and differential evolution algorithm. The objective of the proposed method is to determine an optimal reduced-order model for the given original higher-order linear continuous-time system by minimizing the integral square error (ISE) between their step responses. The method has significant features like easy implementation, good performance, numerically stable and fast convergence. Applicability and efficacy of the method are shown by illustrating an IEEE type-1 DC excitation system, and by a typical ninth-order system taken from the literature. The results obtained from the proposed algorithm are compared with many familiar and recent reduction techniques that are available in the literature, in terms of step ISE values and impulse response energies of the models. Furthermore step and frequency responses are also plotted.