H∞ optimal sampled-data state observer design

Access Full Text

H∞ optimal sampled-data state observer design

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

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

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:
 
 
 
 
 
IEE Proceedings - Control Theory and Applications — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

© IEE
Published

The problem of sampled-data state reconstruction in linear time invariant systems is considered. A new full order observer structure that can generate intersample state estimation is introduced. The observer synthesis is carried out using the H∞ framework and is shown to have some important advantages over the classical lifting technique that has been used to study similar problems. A simulation example illustrates the application of the proposed design in the fast rate fault detection problem.

Inspec keywords: H∞ control; control system synthesis; linear systems; observers; sampled data systems

Other keywords: fault detection problem; H∞ optimal sampled-data state observer design; intersample state estimation; full order observer structure; observer synthesis; linear time invariant systems

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

References

    1. 1)
      • J. Wüunnenberg , P.M. Frank , S.G. Tzafestas , M.G. Singh , G. Schmidt . (1987) Sensor fault using detection via robust observer, System fault diagnosis, reliability and related knowledge-based approaches.
    2. 2)
      • K. Watanabe , D.M. Himmelblau . Instrument fault detection in systems with uncertainty. Int. J. Sys. Sci. , 137 - 158
    3. 3)
      • A.H. Jazwinski . (1970) Stochastic processes and filtering theory.
    4. 4)
    5. 5)
    6. 6)
      • K. Zhou , J.C. Doyle . (1997) Essentials of robust control.
    7. 7)
      • P.M. Frank , J. Wüunnenberg , R.J. Patton , P.M. Frank , R.N. Clark . (1989) Robust fault diagnosis using unknown input schemes, System fault diagnosis in dynamical systems: theory and application.
    8. 8)
    9. 9)
      • J. Chen , R.J. Patton . (1999) Robust model-based fault diagnosis for dynamic systems.
    10. 10)
    11. 11)
      • D.-Z. Zheng . (2006) Linear systems theory.
    12. 12)
      • Rossiter, J.A., Chen, T., Shah, S.L.: `A comparison of lifting and inferential control for multi-rate systems', Proc. Am. Control Conf., 4–6 June 2003, Denver, Colorado.
    13. 13)
      • T. Chen , B. Francis . (1995) Optimal sampled-data control systems.
    14. 14)
      • D.G. Meyer . A parameterization of stabilizing controllers for multirate sampled-data systems. IEEE Trans. Autom. Control , 233 - 236
    15. 15)
    16. 16)
      • A. Lynch . (2004) Control sytems II (Lab manual).
    17. 17)
      • T. Kailath . (1980) Linear systems.
    18. 18)
    19. 19)
      • C.T. Chen . (1998) Linear systems theory and design.
    20. 20)
      • R. Weber , C. Brosilow . The use of secondary measurements to improve control. AIChE J. , 614 - 623
http://iet.metastore.ingenta.com/content/journals/10.1049/ip-cta_20050051
Loading

Related content

content/journals/10.1049/ip-cta_20050051
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading