Analytic hierarchy process-based model reduction of higher order continuous systems using sine cosine algorithm

Abstract

The analysis of higher order systems is tedious and cumbersome task. This motivated analysts to reduce higher order systems into lower order models using mathematical approaches. In this paper, an analytic hierarchy process (AHP)-based approximation of stable higher order systems to stable lower order models using sine cosine algorithm (SCA) is presented. The stable approximant is deduced by minimising the relative errors in between time moments and Markov parameters of the system and its approximant. In order to match the steady states of the system and its approximant, the first time moment of the system is retained in the approximant. AHP is utilised to convert multi-objective problem of minimisation of errors in between time moments and Markov parameters into a single objective problem by proper assignment of weights. To ensure the stability of the approximant, Hurwitz criterion is utilised. The systematic nature and efficacy of the proposed technique is validated by deriving approximants for three different test systems.