Efficient Technique for Model Order Reduction Retaining Non-Minimum Phase Characteristics using Clustering Dominant Pole-Zero Algorithm

Abstract

<span style="font-family: 'Times New Roman',serif; font-size: 10pt; -ms-layout-grid-mode: line; mso-fareast-font-family: 'Times New Roman'; mso-ansi-language: EN-GB; mso-fareast-language: EN-US; mso-bidi-language: AR-SA;" lang="EN-GB">Model Order Reduction (MOR) challenges a high dimensional problem and plays a key role in areas where dynamic simulation studies are necessary for modern simulation strategy. Many conventional reduction methods namely, reduced order models based on Least Square Method (LSM), Balanced Truncation, Hankel Norm reduction, Dominant Pole Algorithm (DPA) and CDPA method have been developed in the field of control theory. Among these, recently proposed Clustering Dominant Pole Algorithm (CDPA) is able to compute the full set of dominant poles and their cluster center efficiently. In this paper, a hybrid algorithm for model order reduction known as Clustering Dominant Pole-Zero Algorithm (CDPZA) is proposed to identify and preserve the dominant zeros of the processes exhibiting non-minimum phase behaviour. The CDPZA method combines the features of clustering method and DPA. Further, the cluster centers of the dominant zeros in the numerator polynomial are determined using factor division algorithm. The Benchmark HiMAT system of 6<sup>th</sup> order is considered for testing and validation of the proposed algorithm. The simulation studies are carried out to show the efficacy of the proposed algorithm over conventional MOR algorithms.</span>