2016, 6(1): 73-90. doi: 10.3934/naco.2016.6.73

Projection-based model reduction for time-varying descriptor systems: New results

1. 

Department of Mathematics and Physics, North South University, Dhaka, Bangladesh

Received  July 2015 Revised  January 2016 Published  January 2016

We have presented a Krylov-based projection method for model reduction of linear time-varying descriptor systems in [13] which was based on earlier ideas in the work of J. Philips [17] and others. This contribution continues that work by presenting more details of linear time-varying descriptor systems and new results coming from real fields of application. The idea behind the proposed procedure is based on a multipoint rational approximation of the monodromy matrix of the corresponding differential-algebraic equation. This is realized by orthogonal projection onto a rational Krylov subspace. The algorithmic realization of the method employs recycling techniques for shifted Krylov subspaces and their invariance properties. The proposed method works efficiently for macro-models, such as time varying circuit systems and models arising in network interconnection, on limited frequency ranges. Bode plots and step response are used to illustrate both the performance and accuracy of the reduced-order model.
Citation: Mohammad-Sahadet Hossain. Projection-based model reduction for time-varying descriptor systems: New results. Numerical Algebra, Control & Optimization, 2016, 6 (1) : 73-90. doi: 10.3934/naco.2016.6.73
References:
[1]

Z. Bai, Krylov subspace techniques for reduced-order modeling for large-scale dynamical system,, App. Numer. Math, 43(1-2) (2002), 1. doi: 10.1016/S0168-9274(02)00116-2. Google Scholar

[2]

P. Benner, Solving large-scale control problems,, IEEE Control System Magazine, 24 (2004), 44. Google Scholar

[3]

P. Benner, Numerical linear algebra for model reduction in control and simulation,, GAMM Mitteilungen, 23 (2006), 275. doi: 10.1002/gamm.201490034. Google Scholar

[4]

S. Bittanti and P. Colaneri, Periodic Systems: Filtering and Control,, 1st edition, (2009). Google Scholar

[5]

H. G. Brachtendorf, Theorie und Analyse von Autonomen und Quasiperiodisch Angeregten Elektrischen Netzwerken. Eine Algorithmisch Orientierte Betrachtung,, Habilitation thesis, (2001). Google Scholar

[6]

R. L. Burden and J. D. Faires, Numerical Analysis,, Ninth edition, (2011). Google Scholar

[7]

I. Elfadel and D. D. Ling, A block Arnoldi algorithm for multipoint passive model-order reduction of multiport RLC networks,, in IEEE/ACM International Conference on Computer-Aided Design, (1997), 66. Google Scholar

[8]

R. Freund, Model reduction methods based on Krylov subspaces,, Acta Numerica, 12 (2003), 267. doi: 10.1017/S0962492902000120. Google Scholar

[9]

G. Golub and C. Van Loan, Matrix Computations,, 3rd edition, (1996). Google Scholar

[10]

E. J. Grimme, Kryloy projection methods for model reduction,, Ph.D. thesis, (1997). Google Scholar

[11]

S. Gugercin, An iterative SVD-KRYLOV based method for model reduction of large-scale dynamical systems,, in 44'th IEEE Conference on Decision and Control and the European control conference, (2005), 5905. Google Scholar

[12]

S. Gugercin, A. C. Antoulas and C. Beattie, H2 model reduction for large-scale linear dynamical systems,, SIAM J. Matrix Anal. Appl, 30 (2008), 609. doi: 10.1137/060666123. Google Scholar

[13]

M.-S. Hossain and P. Benner, Projection-based model reduction for time-varying descriptor systems using recycled Krylov subspaces,, in Appllied Mathematics and Mechanics, (2008), 10081. Google Scholar

[14]

M. Nakhla and E. Gad, Efficient model reduction of linear time-varying systems via compressed transient system function,, in Conference on Design, (2002), 916. Google Scholar

[15]

M. Nakhla and E. Gad, Efficient model reduction of linear periodically time-varying systems via compressed transient system function,, IEEE Transactions on Circuit and Systems, 52 (2005), 1188. doi: 10.1109/TCSI.2005.846661. Google Scholar

[16]

T. Penzl, Algorithms for model reduction of large dynamical systems,, Linear Algebra Appl., 415 (2006), 322. doi: 10.1016/j.laa.2006.01.007. Google Scholar

[17]

J. Phillips, Model reduction of time-varying linear systems using multipoint Krylov-subspace projectors,, in International Conference on Computer-Aided Design, (1998), 96. Google Scholar

[18]

J. Phillips, Projection-based approaches for model reduction of weakly nonlinear time-varying systems,, IEEE Trans. Computer-Aided Design, 22 (2003), 171. Google Scholar

[19]

A. Rahman and M. S. Hossain, Svd-Krylov based model reduction for time-varying periodic descriptor systems,, in 2nd International Conference on Electrical Engineering and Information Technology, (2015). Google Scholar

[20]

J. Roychowdhury, Reduced-order modeling of time-varying systems,, IEEE Control Systems Magazine, 46 (1999), 1273. Google Scholar

[21]

J. Roychowdhury, Reduced-order modelling of linear time-varying systems,, in ASP-DAC '99. Asia and South Pacific, (1999), 53. Google Scholar

[22]

Y. Saad, Overview of Krylov subspace methods with applications to control problems,, in International Symposium MTNS-89 on Signal Processing, (1990). Google Scholar

[23]

Y. Saad, Iterative Methods for Sparse Linear Systems,, 2nd edition, (2003). doi: 10.1137/1.9780898718003. Google Scholar

[24]

B. Salimbahrami, B. Lohmann, T. Bechtold and J. Korvink, Two-sided Arnoldi algorithm and its application in order reduction of MEMS,, in 4th Fourth International Conference on Mathematical Modelling (eds. I. Troch and F. Breitenecker), (2003), 1021. Google Scholar

[25]

S. B. Salimbahrami, Structure Preserving Order Reduction of Large Scale Second Order Models,, Ph.D. thesis, (2005). Google Scholar

[26]

R. E. Skelton, M. Oliveira and J. Han, System modeling and model reduction,, Paper available from: , (). Google Scholar

[27]

T. Stykel, Low-rank iterative methods for projected generalized Lyapunov equations,, Electron. Trans. Numer. Anal., 30 (2008), 187. Google Scholar

[28]

R. Telichevesky, J. White and K. Kundert, Efficient steady-state analysis based on matrix-free Krylov-subspace methods,, in 32rd Design Automation Conference, (1995), 480. Google Scholar

[29]

R. Telichevesky, J. White and K. Kundert, Efficient AC and noise analysis of two-tone RF circuits,, in 33rd annual Design Automation Conference, (1996), 292. Google Scholar

[30]

A. A. Vaidyanathan, Multirate digital filters, filters banks, polyphase networks, and applications: A tutorial,, in IEEE Proceedings, (1990), 56. Google Scholar

[31]

E. Wachspress, The ADI Model Problem,, 1995, (). Google Scholar

[32]

B. Yang and D. Feng, Efficient finite-difference method for quasi-periodic steady-state and small signal analyses,, in IEEE/ACM International Conference on Computer-Aided Design, (2000), 272. Google Scholar

[33]

L. Zadeh, Frequency analysis of variable networks,, IEEE Transactions on Circuits and Systems, 38 (1950), 291. Google Scholar

show all references

References:
[1]

Z. Bai, Krylov subspace techniques for reduced-order modeling for large-scale dynamical system,, App. Numer. Math, 43(1-2) (2002), 1. doi: 10.1016/S0168-9274(02)00116-2. Google Scholar

[2]

P. Benner, Solving large-scale control problems,, IEEE Control System Magazine, 24 (2004), 44. Google Scholar

[3]

P. Benner, Numerical linear algebra for model reduction in control and simulation,, GAMM Mitteilungen, 23 (2006), 275. doi: 10.1002/gamm.201490034. Google Scholar

[4]

S. Bittanti and P. Colaneri, Periodic Systems: Filtering and Control,, 1st edition, (2009). Google Scholar

[5]

H. G. Brachtendorf, Theorie und Analyse von Autonomen und Quasiperiodisch Angeregten Elektrischen Netzwerken. Eine Algorithmisch Orientierte Betrachtung,, Habilitation thesis, (2001). Google Scholar

[6]

R. L. Burden and J. D. Faires, Numerical Analysis,, Ninth edition, (2011). Google Scholar

[7]

I. Elfadel and D. D. Ling, A block Arnoldi algorithm for multipoint passive model-order reduction of multiport RLC networks,, in IEEE/ACM International Conference on Computer-Aided Design, (1997), 66. Google Scholar

[8]

R. Freund, Model reduction methods based on Krylov subspaces,, Acta Numerica, 12 (2003), 267. doi: 10.1017/S0962492902000120. Google Scholar

[9]

G. Golub and C. Van Loan, Matrix Computations,, 3rd edition, (1996). Google Scholar

[10]

E. J. Grimme, Kryloy projection methods for model reduction,, Ph.D. thesis, (1997). Google Scholar

[11]

S. Gugercin, An iterative SVD-KRYLOV based method for model reduction of large-scale dynamical systems,, in 44'th IEEE Conference on Decision and Control and the European control conference, (2005), 5905. Google Scholar

[12]

S. Gugercin, A. C. Antoulas and C. Beattie, H2 model reduction for large-scale linear dynamical systems,, SIAM J. Matrix Anal. Appl, 30 (2008), 609. doi: 10.1137/060666123. Google Scholar

[13]

M.-S. Hossain and P. Benner, Projection-based model reduction for time-varying descriptor systems using recycled Krylov subspaces,, in Appllied Mathematics and Mechanics, (2008), 10081. Google Scholar

[14]

M. Nakhla and E. Gad, Efficient model reduction of linear time-varying systems via compressed transient system function,, in Conference on Design, (2002), 916. Google Scholar

[15]

M. Nakhla and E. Gad, Efficient model reduction of linear periodically time-varying systems via compressed transient system function,, IEEE Transactions on Circuit and Systems, 52 (2005), 1188. doi: 10.1109/TCSI.2005.846661. Google Scholar

[16]

T. Penzl, Algorithms for model reduction of large dynamical systems,, Linear Algebra Appl., 415 (2006), 322. doi: 10.1016/j.laa.2006.01.007. Google Scholar

[17]

J. Phillips, Model reduction of time-varying linear systems using multipoint Krylov-subspace projectors,, in International Conference on Computer-Aided Design, (1998), 96. Google Scholar

[18]

J. Phillips, Projection-based approaches for model reduction of weakly nonlinear time-varying systems,, IEEE Trans. Computer-Aided Design, 22 (2003), 171. Google Scholar

[19]

A. Rahman and M. S. Hossain, Svd-Krylov based model reduction for time-varying periodic descriptor systems,, in 2nd International Conference on Electrical Engineering and Information Technology, (2015). Google Scholar

[20]

J. Roychowdhury, Reduced-order modeling of time-varying systems,, IEEE Control Systems Magazine, 46 (1999), 1273. Google Scholar

[21]

J. Roychowdhury, Reduced-order modelling of linear time-varying systems,, in ASP-DAC '99. Asia and South Pacific, (1999), 53. Google Scholar

[22]

Y. Saad, Overview of Krylov subspace methods with applications to control problems,, in International Symposium MTNS-89 on Signal Processing, (1990). Google Scholar

[23]

Y. Saad, Iterative Methods for Sparse Linear Systems,, 2nd edition, (2003). doi: 10.1137/1.9780898718003. Google Scholar

[24]

B. Salimbahrami, B. Lohmann, T. Bechtold and J. Korvink, Two-sided Arnoldi algorithm and its application in order reduction of MEMS,, in 4th Fourth International Conference on Mathematical Modelling (eds. I. Troch and F. Breitenecker), (2003), 1021. Google Scholar

[25]

S. B. Salimbahrami, Structure Preserving Order Reduction of Large Scale Second Order Models,, Ph.D. thesis, (2005). Google Scholar

[26]

R. E. Skelton, M. Oliveira and J. Han, System modeling and model reduction,, Paper available from: , (). Google Scholar

[27]

T. Stykel, Low-rank iterative methods for projected generalized Lyapunov equations,, Electron. Trans. Numer. Anal., 30 (2008), 187. Google Scholar

[28]

R. Telichevesky, J. White and K. Kundert, Efficient steady-state analysis based on matrix-free Krylov-subspace methods,, in 32rd Design Automation Conference, (1995), 480. Google Scholar

[29]

R. Telichevesky, J. White and K. Kundert, Efficient AC and noise analysis of two-tone RF circuits,, in 33rd annual Design Automation Conference, (1996), 292. Google Scholar

[30]

A. A. Vaidyanathan, Multirate digital filters, filters banks, polyphase networks, and applications: A tutorial,, in IEEE Proceedings, (1990), 56. Google Scholar

[31]

E. Wachspress, The ADI Model Problem,, 1995, (). Google Scholar

[32]

B. Yang and D. Feng, Efficient finite-difference method for quasi-periodic steady-state and small signal analyses,, in IEEE/ACM International Conference on Computer-Aided Design, (2000), 272. Google Scholar

[33]

L. Zadeh, Frequency analysis of variable networks,, IEEE Transactions on Circuits and Systems, 38 (1950), 291. Google Scholar

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