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Model Reduction by New Clustering Method and Frequency Response Matching

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Abstract

In this paper, mixed method of linear, time-invariant system model reduction method is suggested. A novel clustering algorithm based on Lehmer measure is utilized in the proposed method to obtain the reduced-order denominator polynomial. The selection of poles to form cluster center is based on the viewpoint of important poles contributing to the system is preserved by dominant pole algorithm. Having obtained the denominator polynomial of the reduced model, the coefficient of the numerator is found using the frequency response matching method. The reduction algorithm is fully computer oriented. The reduced model is stable if the original model is stable. Moreover, this method gives a good quality approximation in both the transient and the steady-state responses of the original system.

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References

  • Appiah, R. K. (1979). Pade methods of Hurwitz polynomial approximation with application to linear system reduction. International Journal of Control, 29(1), 39–48.

    Article  MATH  MathSciNet  Google Scholar 

  • Bistritz, Y., & Langholz, G. (1979). Model reduction by Chebyshev polynomial techniques. IEEE Transactions on Automatic Control, 24(5), 741–747.

    Article  MATH  Google Scholar 

  • Bultheel, A., & Barel, M. V. (1986). Padé techniques for model reduction in linear system theory: A survey. Journal of Computational and Applied Mathematics, 14, 401–438.

    Article  MATH  MathSciNet  Google Scholar 

  • Chahlaoui, Y., & Van Dooren, P. (2005). Benchmark examples for model reduction of linear time-invariant dynamical systems. In P. Benner, V. Mehrmann, & Sorensen (Eds.), Dimension reduction of large-scale systems (pp. 379–392). Berlin, Heidelberg: Springer-Verlag.

  • Chen, C. F., & Shieh, L. S. (1970). An algebraic method for control systems design. International Journal of Control, 11(5), 717–739.

    Article  MATH  Google Scholar 

  • Chen, T. C., Chang, C. Y., & Han, K. W. (1979). Reduction of transfer functions by the stability-equation method. Journal of the Franklin Institute, 308(4), 389–404.

    Article  MATH  MathSciNet  Google Scholar 

  • Chen, T. C., Chang, C. Y., & Han, K. W. (1980a). Model reduction using the stability-equation method and the continued-fraction method. International Journal of Control, 32(1), 81–94.

    Article  MATH  MathSciNet  Google Scholar 

  • Chen, T. C., Chang, C. Y., & Han, K. W. (1980b). Model reduction using the stability-equation method and the Padé approximation method. Journal of the Franklin Institute, 309(6), 473–490.

    Article  MATH  MathSciNet  Google Scholar 

  • Chuang, S. C. (1976). Homographic transformation for the simplification of continuous-time transfer functions by Padé approximation. International Journal of Control, 23(6), 821–826.

    Article  MATH  MathSciNet  Google Scholar 

  • Desai, S. R., & Prasad, R. (2013). A new approach to order reduction using stability equation and big bang big crunch optimization. Systems Science & Control Engineering, 1(1), 20–27.

    Article  Google Scholar 

  • Hutton, M., & Friedland, B. (1975). Routh approximations for reducing order of linear, time-invariant systems. IEEE Transactions on Automatic Control, 20(3), 329–337.

    Article  MATH  MathSciNet  Google Scholar 

  • Komarasamy, R., Albhonso, N., & Gurusamy, G. (2011). Order reduction of linear systems with an improved pole clustering. Journal of Vibration and Control, 18(12), 1876–1885.

    Article  MathSciNet  Google Scholar 

  • Krishnamurthi, V., & Seshadri, V. (1976). A simple and direct method of reducing order of linear systems using routh approximations in the frequency domain. IEEE Transactions on Automatic Control, 21(5), 797–799.

    Article  MathSciNet  Google Scholar 

  • Krishnamuthy, V., & Seshadri, V. (1978). Model reduction using the Routh stability criterion. IEEE Transactions on Automatic Control, 23, 729–731.

    Article  Google Scholar 

  • Langhoz, G., & Bistrltz, Y. (1980). Model reduction of dynamic systems over a frequency interval\(\dagger \). International Journal of Control, 31(1), 51–62.

    Article  MathSciNet  Google Scholar 

  • Lin, P. L., & Wu, Y. C. (1982). Reduction of transfer functions from the stability-equation method and complex curve fitting. Journal of the Franklin Institute, 314(2), 109–121.

    Article  MATH  Google Scholar 

  • Lucas, T. (1983). Factor division: A useful algorithm in model reduction. Control Theory and Applications., 130(6), 1981–1983.

    Google Scholar 

  • Martins, N., & Quintão, P. E. M. (2003). Computing dominant poles of power system multlvariable transfer functions. IEEE Transactions on Power Systems, 11(1), 152–159.

    Article  Google Scholar 

  • Mittal, A. K., Prasad, R., & Sharama, S. P. (2004). Reduction of linear dynamic system using an error minimization technique. Journal of the Institution of Engineers (India), 84, 201–204.

    Google Scholar 

  • Mukherjee, S., & Mittal, R. C. (2005). Model order reduction using response-matching technique. Journal of the Franklin Institute, 342(5), 503–519.

    Article  MATH  MathSciNet  Google Scholar 

  • Ouyang, M., Liaw, C., & Pan, C. (1987). Model reduction by power decomposition and frequency response matching. IEEE Transactions on Automatic Control, 32(1), 59–62.

    Article  MATH  Google Scholar 

  • Pal, J. (1983). Improved Pade approximants using stability equation method. Electronics Letters, 19(11), 3–4.

    Article  Google Scholar 

  • Parmar, G., Mukherjee, S., & Prasad, R. (2007a). Reduced order modelling of linear multivariable systems using particle swarm optimisation technique. International Journal of Innovative Computing and Applications, 1(2), 128.

    Article  Google Scholar 

  • Parmar, G., Mukherjee, S., & Prasad, R. (2007b). System reduction using factor division algorithm and eigen spectrum analysis. Applied Mathematical Modelling, 31(11), 2542–2552.

    Article  MATH  Google Scholar 

  • Parthasarathy, R., & John, S. (1978). System reduction using Cauer continued fraction expansion about \(s=0\) and \(s=\infty \) alternately. Electronics Letters, 18(23), 9–10.

    Google Scholar 

  • Philip, B., & Pal, J. (2010). An evolutionary computation based approach for reduced order modelling of linear systems. IEEE International Conference on Computational Intelligence and Computing Research, 2010, 1–8.

    Article  Google Scholar 

  • Prasad, R., & Pal, J. (1991). Use of continued fraction expansion for stable reduction of linear multivariable systems. Institution of Engineers India Part Electrical Engineering Division, 72, 43–43.

    Google Scholar 

  • Rao, S. V., & Lammba, S. S. (1974). A new frequency domain technique for the simplification of linear dynamic systems. International Journal of Control, 20(1), 71–79.

    Article  Google Scholar 

  • Reddy, A. S. S. R. (1976). A method for frequency domain simplification of transfer functions. International Journal of Control, 23(3), 403–408.

    Article  MATH  Google Scholar 

  • Saadvandi, M., Meerbergen, K., & Jarlebring, E. (2012). On dominant poles and model reduction of second order time-delay systems. Applied Numerical Mathematics, 62(1), 21–34.

    Article  MATH  MathSciNet  Google Scholar 

  • Shamash, Y. (1980a). Failure of the Routh–Hurwitz method of reduction. IEEE Transactions on Automatic Control, 25(2), 313–314.

    Article  MATH  MathSciNet  Google Scholar 

  • Shamash, Y. (1980b). Stable biased reduced order models using the Routh method of reduction. International Journal of Systems Science, 11(5), 641–654.

  • Singh, J., Vishwakarma, C. B., & Chattterjee, K. (2016). Biased reduction method by combining improved modified pole clustering and improved Pade approximations. Applied Mathematical Modelling, 40(2), 1418–1426.

    Article  MathSciNet  Google Scholar 

  • Sinha, A. K., & Pal, J. (1990). Simulation based reduced order modelling using a clustering technique. Computers & Electrical Engineering, 16(3), 159–169.

    Article  Google Scholar 

  • Skogestad, S., & Postlethwaite, I. (2007). Multivariable feedback control: Analysis and design (Vol. 2). New York: BOOK, Wiley.

    MATH  Google Scholar 

  • Soloklo, H. N., & Farsangi, M. M. (2013). Chebyshev rational functions approximation for model order reduction using harmony search, 20, 771–777.

    Google Scholar 

  • Soloklo, H. N., & Farsangi, M. M. (2015). Model order reduction by using Legendre expansion and harmony search algorithm. Majlesi Journal of Electrical Engineering, 9(1), 25–35.

    Google Scholar 

  • Soloklo, H. N., Hajmohammadi, R., & Farsangi, M. M. (2015). Model order reduction based on moment matching using Legendre wavelet and harmony search algorithm. Iranian Journal of Science and Technology, Transactions of Electrical Engineering, 39(E1), 39–54.

    Google Scholar 

  • Soloklo, H. N., Nali, O., & Farsangi, M. M. (2014). Model reduction by Hermite polynomials and genetic algorithm. Journal of Mathematics and Computer Science, 9, 188–202.

    Google Scholar 

  • Stolarsky, K. B. (1996). Holder means, Lehmer Means, and \(\text{ x }^{-1}\) log cosh x. Journal of Mathematical Analysis and Applications, 202, 810–818.

    Article  MATH  MathSciNet  Google Scholar 

  • Vishwakarma, C. B., & Prasad, R. (2008). Clustering method for reducing order of linear system using Pade approximation. IETE Journal of Research., 54(5), 326–330.

    Article  Google Scholar 

  • Vishwakarma, C. B., & Prasad, R. (2009). MIMO system reduction using modified pole clustering and genetic algorithm. Modelling and Simulation in Engineering, 2009, 1–5.

    Article  Google Scholar 

  • Wan, B.-W. (1981). Linear model reduction using Mihailov criterion and Padé approximation technique. International Journal of Control, 33(6), 1073–1089.

    Article  MATH  MathSciNet  Google Scholar 

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Correspondence to Sharad Kumar Tiwari.

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Tiwari, S.K., Kaur, G. Model Reduction by New Clustering Method and Frequency Response Matching. J Control Autom Electr Syst 28, 78–85 (2017). https://doi.org/10.1007/s40313-016-0282-y

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  • DOI: https://doi.org/10.1007/s40313-016-0282-y

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