Model Order Reduction Using Krylov-Subspace Based Two-sided Arnoldi Algorithm

0*, Awadhesh Kumar**
* PG Scholar, Department of Electrical Engineering, Madan Mohan Malviya University of Technology, Gorakhpur, India.
** Assistant Professor, Department of Electrical Engineering, Madan Mohan Malviya University of Technology, Gorakhpur, India.
Periodicity:February - April'2016
DOI : https://doi.org/10.26634/jic.4.2.4880

Abstract

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.

Keywords

Model Order Reduction (MOR), Krylov-subspace, One-sided Arnoldi, Two-sided Arnoldi, Moment Matching, Time-moments, Markov-Parameter

How to Cite this Article?

Shahi, N., and Kumar, A. (2016). Model Order Reduction Using Krylov-Subspace Based Two-sided Arnoldi Algorithm. i-manager’s Journal on Instrumentation and Control Engineering, 4(2), 29-38. https://doi.org/10.26634/jic.4.2.4880

References

[1]. Salimbahrami, B., Lohmann, B., Bechtold, T., and Korvink, J. G., (2003). "Two-sided Arnoldi Algorithm and Its Application in Order Reduction of MEMS". in Proceedings of 4th Mathmod, pp. 1021–1028.
[2]. Salimbahrami, B. and Lohmann, B., (2002). "Two sided Arnoldi Method for Model Order Reduction of High Order MIMO Systems”. Retrived from http://www.iat.unibren.de/ mitarbeiter/salimbahrami/2arnoldi mimo.pdf
[3]. Freund, R.W., (2000). "Krylov Subspace Methods for Reduced Order Modeling in Circuit Simulation". Journal on Computer and Applied Mathematics, Vol.123, pp.395–421.
[4]. A.C. Antoulas, (2005). Approximation of Large-Scale Dynamical Systems. SIAM Philadelphia, PA.
[5]. A.C. Antoulas, D.C. Sorenson, and S. Gugercin, (2001). "A Survey of Model Reduction for Large Scale Systems". Contemporary Mathematics, AMS Publications, Vol.280, pp.193-219.
[6]. S. Gugercin, and J. Rebecca Li, (2003). "Smith type methods for Balanced Truncation of Large Sparse Systems". in Proceedings of Dimension Reduction of Large Scale Systems, Germany.
[7]. D. Chaniotis, and M.A. Pai, (2005). "Model Reduction in Power Systems Using Krylov Subspace Methods". IEEE Transactions on Power Systems, Vol.20, No.2, pp.888-894.
[8]. Freund, R.W, (2000). "Passive Reduced Order Modelling via Krylov Subspace Methods". Numerical Analysis Manuscript.
[9]. W.E. Arnoldi, (1951). "The Principle of Minimized Iteration in Solution of the Matrix Eigen value Problem". Quarterly Journal of Mechanics and Applied Mathematics, Vol.9, pp.17–29.
[10]. T. Penzl, (1999). "Algorithms for Model Reduction of La rge Dynamics Systems". Linear Algebra and its Applications, Vol. 415, No. 2,pp.322-343.
[11]. Cullum, J. and Zhang, T, (2002). "Two-sided Arnoldi and Nonsymmetric Lanczos Algorithms". SIAM Journal on Matrix Analysis and Applications, Vol.24, No.2, pp. 303-309.
[12]. E.J. Grimme, (2002). “Krylov Projection Methods for Model Reduction”. PhD thesis, Department of Electrical Engineering, University of Illinois at Urbana Campaign.
[13]. Salimbahrami, B. and Lohmann, B., (2002). "Krylov subspace methods in Linear Model Order Reduction: Introduction and Invariance properties". Scientific report, Retrived from http://www.iat.uni-bremen.de/mitarbeiter/ salimbahrami/Invariance properties.pdf
[14]. G. Parmar, S. Mukherjee and R. Prasad, (2007). "system Reduction Using Factor Division Algorithm and EigenSpectrum Analysis". Applied Mathematical Modeling, Elsevier, Vol.31, pp.2542-2552.
[15]. Moore, B.C, (1981). "Principal Component Analysis in Linear Systems: Controllability, Observability and Model Reduction". IEEE Transactions on Automatic Control, Vol.26, pp.17-31.
If you have access to this article please login to view the article or kindly login to purchase the article

Purchase Instant Access

Single Article

North Americas,UK,
Middle East,Europe
India Rest of world
USD EUR INR USD-ROW
Online 200 35 35 200 15
Pdf 35 35 200 20
Pdf & Online 35 35 400 25

Options for accessing this content:
  • If you would like institutional access to this content, please recommend the title to your librarian.
    Library Recommendation Form
  • If you already have i-manager's user account: Login above and proceed to purchase the article.
  • New Users: Please register, then proceed to purchase the article.