JIC_V4_N2_RP5 Nikku Shahi Awadhesh Kumar Journal on Instrumentation & Control Engineering 2321 – 1148 4 2 29 38 Model Order Reduction (MOR), Krylov-subspace, One-sided Arnoldi, Two-sided Arnoldi, Moment Matching, Time-moments, Markov-Parameter Model Order Reduction (MOR) plays an important role in determining a Reduced Order Model (ROM) from a large scale system, while preserving their input-output behaviour. A Reduced Order Model is a lower dimensional computational model which can faithfully reproduce the essential feature of a higher dimensional model. This paper presents, the overview of Model Order Reduction with emphasis on Krylov-Subspace based technique and its algorithm. Krylovsubspace methods are well known and used in different applications of MOR. Krylov-subspaces replaces the large and expensive model by a smaller model, with excellent approximating properties and at the same time by means of efficient computational approach. The paper overviewed on the algorithms of Krylov-subspace technique that is Arnoldi algorithm and Two-sided Arnoldi algorithm which is used for obtaining the reduced-order models of high-order linear time invariant systems with an appropriate implicitly matching of Time Moments and Markov Parameters. Further, three numerical examples have been carried out to obtain their reduced order models with the preservation of stability. February - April 2016 Copyright © 2016 i-manager publications. All rights reserved. i-manager Publications http://www.imanagerpublications.com/Article.aspx?ArticleId=4880