Improved pole clustering-based LTI system reduction using a factor division algorithm. (2nd January 2019)

• Record Type: Journal Article
• Authors: Singh, Chhabindra Nath; Kumar, Deepak; Samuel, Paulson
• Is Part Of: International journal of modelling & simulation. Volume 39:Number 1(2019)
• Page Start: 1
• Page End: 13
• Publication Date: 2019-01-02
• Subjects: Factor division -- improved pole clustering -- integral absolute error -- integral square error -- model order reduction; Mathematical models -- Periodicals; Simulation methods -- Periodicals; Mathematical models; Simulation methods; Periodicals; 003.3
• DOI: 10.1080/02286203.2018.1459373
• Languages: English
• ISSNs: 0228-6203

Abstract

Abstract: In this paper, a novel technique is proposed to approximate the high-order linear continuous-time and discrete-time systems. The proposed methodology is based on the improved pole clustering (IPC) and factor division algorithm (FDA). The IPC technique is employed to estimate the coefficients of reduced-order denominator polynomial while FDA computes the coefficients of numerator polynomial. The distinctive feature of the proposed scheme is that, the approximants turn out to be always stable, if the original high-order system (HOS) is stable. The efficacy of the proposed scheme is tested by considering six numerical examples from the existing literature. Further, the superiority of the proposed technique over existing techniques of order reduction is verified through performance indices, time and frequency responses.