Optimal model reduction of non-causal systems via least squares frequency fitting

Access Full Text

Optimal model reduction of non-causal systems via least squares frequency fitting

For access to this article, please select a purchase option:

Buy article PDF
£12.50
(plus tax if applicable)
Buy Knowledge Pack
10 articles for £75.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
IET Control Theory & Applications — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

A new model reduction method, based on frequency fitting, is proposed for single-input discrete-time singular (non-causal) systems. The reduced-order models are obtained by minimising the least square frequency response error between the original system and the reduced-order model. Finally, the method is illustrated by a numerical example and is compared with other related techniques.

Inspec keywords: frequency response; reduced order systems; discrete time systems; least squares approximations

Other keywords: least square frequency response; reduced-order models; single-input discrete-time singular systems; least squares frequency fitting; optimal model reduction; noncausal systems

Subjects: Discrete control systems; Control system analysis and synthesis methods; Simulation, modelling and identification

References

    1. 1)
    2. 2)
    3. 3)
      • S. Sunder , R.P. Ramachandran . A unified and efficient least-squares design of linear phase nonrecursive filters. Signal Process. , 41 - 53
    4. 4)
    5. 5)
    6. 6)
      • F.L. Lewis , M.A. Christodoulou , B.G. Mertzios , K. Ozcaldiran . Chained aggregation of singular system. IEEE Trans. Autom. Control , 9 , 1007 - 1012
    7. 7)
      • J. Wang , W.Q. Liu , Q.L. Zhang . Model reduction for singular systems via covariance approximation. Opt. Control Appl. Methods , 6 , 263 - 278
    8. 8)
      • S. Gugercin , A.C. Antoulas . Model reduction of large-scale systems by least squares. Linear Algeb. Appl. , 290 - 321
    9. 9)
    10. 10)
      • Zhang, Q.L., Sreeram, V., Wang, G., Liu, W.Q.: `ℋ', Proc. Am. Control Conf., 2002, p. 1168–1173.
    11. 11)
      • K. Perev , B. Shafai . Balanced realisation and model reduction of singular systems. Int. J. Syst. Sci. , 6 , 1039 - 1052
    12. 12)
      • S. Xu , J. Lam , W.Q. Liu , Q.L. Zhang . ℋ∞ Model reduction for continuous time singular systems. IEE Proc. , 6 , 637 - 641
    13. 13)
      • M. Jamshidi . (1983) Large scale systems: modeling and Control.
    14. 14)
      • H. Löffler , W. Marquardt . Order reduction of non-linear differential algebraic process models. J. Process Control , 32 - 40
    15. 15)
      • S. Sunder , R.P. Ramachandran . Design of nonrecursive filters satisfying arbitrary magnitude and phase specifications using a least-squares approach. IEEE Trans. Circuits Syst. - II , 11 , 711 - 716
    16. 16)
      • Mohammad, A.A.: `Modeling issues and the Lyapunov equations in dynamical control systems', 1992, PhD, The University of Akron.
    17. 17)
      • L. Petzold , L. Jay , J. Yen . Numerical solution of highly oscillatory ordinary differential equations. Acta Numerica , 437 - 484
    18. 18)
      • L. Dai . (1989) Singular control systems.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-cta_20050406
Loading

Related content

content/journals/10.1049/iet-cta_20050406
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading