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Reseach Article

Order Reduction of Linear Dynamic System Using MATLAB Programming Method

Published on None 2011 by D.Devi, P.Poongodi
International Conference on VLSI, Communication & Instrumentation
Foundation of Computer Science USA
ICVCI - Number 15
None 2011
Authors: D.Devi, P.Poongodi
4225c409-9f7b-4c23-94e5-27f6bf4750a9

D.Devi, P.Poongodi . Order Reduction of Linear Dynamic System Using MATLAB Programming Method. International Conference on VLSI, Communication & Instrumentation. ICVCI, 15 (None 2011), 23-25.

@article{
author = { D.Devi, P.Poongodi },
title = { Order Reduction of Linear Dynamic System Using MATLAB Programming Method },
journal = { International Conference on VLSI, Communication & Instrumentation },
issue_date = { None 2011 },
volume = { ICVCI },
number = { 15 },
month = { None },
year = { 2011 },
issn = 0975-8887,
pages = { 23-25 },
numpages = 3,
url = { /proceedings/icvci/number15/2743-1565/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 International Conference on VLSI, Communication & Instrumentation
%A D.Devi
%A P.Poongodi
%T Order Reduction of Linear Dynamic System Using MATLAB Programming Method
%J International Conference on VLSI, Communication & Instrumentation
%@ 0975-8887
%V ICVCI
%N 15
%P 23-25
%D 2011
%I International Journal of Computer Applications
Abstract

This paper presents an algorithm for model order reduction of linear dynamic systems using the in MATLAB programming method. The denominator and the numerator coefficients of the reduced order model is obtained by the using pole-zero relationship between given higher order model and the mentioned lower order model. This proposed method is implemented in MATLAB m-file; it retains the original characteristics of the higher order model. It is shown that the proposed method has several advantages like: The reduction procedure is simple compared to other conventional techniques and the error is also minimized. The proposed algorithm has also been used for the order reduction of linear multivariable systems.

References
  1. Genesio, R and Milanese,M.1976.A note on the derivation and use of reduced order models.
  2. Mahmoud,M.S and Singh,M.G.1981.Largescale system modelling
  3. Lamba,S.S,Gorez.,RandBandyopadhyay,B,.1988New Reduction technique by step error minimization for multivariable systems.
  4. Singh,V,.Chandra,.D and Kar,H,.2004.Improved routh pade approximants.
  5. Parmer,G,.Prasad,M,.2007Order reduction of linear dynamic systems using equation method and GA.
  6. Pal, J 1983. Improved pade approximants using stability Equation Method
  7. Parthasarathy,R and Jayasimha, K.N. 1982 System reduction using stability equation method.
  8. Prasad,R,.andPal,J,.1995Multivariable system a pproximationUsing polynomial derivatives.
  9. Davison ,E.J.1966 A method for simplifying linear dynamic systems
  10. Shieh,L.S and Wei,Y.J 1975. A mixed method for multivariable System reduction
  11. Chen,T.C, chang,C.Y and Han.K.W.1980Model reductionUsing the stability equation method.
  12. Hwang, C,1984. Mixed method of routh and ISE criterion approaches for reduced order modelling of continous time systems.
  13. Mukherjee,S.and Mishra, R.N.1987.Order reduction of linear dynamic systems using an error minimization technique.
  14. Puri,N.N and Lan,D.P.1988.Stable model reduction by impulse response error minimization using mihailov criterion and pade’s approximation.
Index Terms

Computer Science
Information Sciences

Keywords

Higher order model Model order reduction MATLAB steady state value