Skip to main content

Competition, Interaction and Control

  • Chapter
  • 265 Accesses

Summary

1 Automatic Control is a technical discipline which has extremely varied fields of application but is only visible through the field of application where it is used. Control competes with other disciplines as provider of an efficient new technology. Interaction with other technologies is necessary in practical situations.

Competition and interaction occur inside the control discipline as well. Interaction between various control techniques is not only necessary but it is also very beneficial for the development of the discipline itself.

The paper will illustrate these aspects via a few practical examples and research developments.

This is a preview of subscription content, log in via an institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Adaptech. WimPIM+(includes, WinPIM, WinREG and WinTRAC) System Identification and Control Software. User’s manual. 4, rue du tour de l’eau, 38400 St. Martin-d’Hères, France, 1996.

    Google Scholar 

  2. B.D.O. Anderson and R.L. Kosut. Adaptive robust control: on-line learning. In Proc. 30th IEEE-CDC, Brighton, UK, 1991.

    Google Scholar 

  3. K.J. Aström. Matching criteria for control and identification. In Proc. European Control Conference, Groningen, The Netherlands, 1993.

    Google Scholar 

  4. R.R. Bitmead. Iterative control design approaches. In Prepr. 12th IFAC World Congress, volume 9, pages 381–384, Sydney, Australia, 1993.

    Google Scholar 

  5. J.C. Doyle, B.A. Francis, and A.R. Tannenbaum. Feedback Control Theory. Mac Milan, N.Y., 1992.

    Google Scholar 

  6. M. Gevers. Towards a joint design of identification and control? In Essays on Control: Perspectives in the Theory and its Applications, pages 111–152. Birkhäuser, Boston, U.S.A., 1993.

    Google Scholar 

  7. M. Gevers. Identification for control. In Prepr. IFAC Symposium ACASP 95, Budapest, Hungary, June 1995.

    Google Scholar 

  8. G.C. Goodwin and K.S. Sin. Adaptive Filtering Prediction and Control. Prentice Hall, N. J., 1984.

    MATH  Google Scholar 

  9. H. Hjalmarsson, M. Gevers, and F. De Bruyne. For model-based control design, closed loop identification gives better performance. Automatica,32(12):16591673, 1996.

    Google Scholar 

  10. A. Karimi and I.D. Landau. Comparison of the closed loop identification methods in terms of the bias distribution. Technical report, LAG, January 1997. To appear in Systems and Control Letters, 1998.

    Google Scholar 

  11. I.D. Landau. System identification and control design. Prentice Hall, Englewood Cliffs, N.J., USA, 1990.

    Google Scholar 

  12. I.D. Landau. Robust digital control of systems with time delay. Int..1. of Control, 62 (2): 325–347, 1995.

    Article  MATH  Google Scholar 

  13. I.D. Landau and A. Karimi. Recursive algorithms for identification in closed loop - a unified approach and evaluation. In Proc. 36 IEEE CDC, Kobe, Japan (extended version: to appear in Automatica 1997 ), December 1996.

    Google Scholar 

  14. I.D. Landau and A. Karimi. An output error recursive algorithm for unbiased identification in closed loop. Automatica, 33 (4), 1997.

    Google Scholar 

  15. I.D. Landau, M. M’Saad and R. Lozano. Adaptive Control Springer Verlag, London, 1997.

    Google Scholar 

  16. I.D. Landau, A. Karimi, A. Voda-Besançon, and D. Rey. Robust digital control of flexible transmissions using the combined pole placement/sensitivity function shaping method. European Journal of Control, 1 (2): 122–133, 1995.

    Google Scholar 

  17. I.D. Landau, J. Langer, D. Rey, and J. Barnier. Robust control of a 360° flexible arm using the combined pole/placement sensitivity function shaping method. IEEE T-CST, 4 (4): 369–383, 1996.

    Google Scholar 

  18. J. Langer and I.D. Landau. Improvement of robust digital control by identification in closed loop, application to a 360° flexible arm. Control Eng. Practice, 4 (8): 1079–1088, 1996.

    Article  Google Scholar 

  19. J. Langer and I.D. Landau. Combined pole placement/sensitivity function shaping method using convex optimization criteria. In Proc. ECC 97, Bruxelles, Belgium, July 1997.

    Google Scholar 

  20. S. Lee, B.D.O. Anderson, R.L. Kosut, and I.M.Y. Mareels. A new approach to adaptive robust control. Int. J. ACASP, 7: 183–211, 1993.

    MATH  Google Scholar 

  21. L. Ljung. System Identification: Theory for the User. Prentice-Hall, Englewood Cliffs, N.J.,USA, 1987.

    Google Scholar 

  22. L. Ljung. Information contents in identification data from closed-loop operation. In Proc. 32th IEEE-CDC, San Antonio, Texas, USA, December 1993.

    Google Scholar 

  23. R. Lozano and X.H. Zhao. Adaptive pole placement without excitation probing signals. IEEE Tran.Automatic Control, 39 (1): 47–58, 1994.

    Article  MathSciNet  MATH  Google Scholar 

  24. A.G.J. Mac Farlane. Information knowledge and control in essays on control. In Essays on Control: Perspectives in the Theory and its Applications, pages 1–28. Birkhäuser, Boston, U.S.A., 1993.

    Google Scholar 

  25. R. Ortega and Y. Tang. Robustness of adaptive controllers–a survey. Automatica, 25 (5): 651–677, 1989.

    Article  MathSciNet  MATH  Google Scholar 

  26. C. Péchoux and Y. Bourande. Régulation active du rapport air/gaz d’un brûleur. In 113ème Congrès du Gaz, Paris, France, September 1996.

    Google Scholar 

  27. R.J.P. Schrama. Accurate models for control design: the necessity of an iterative scheme. IEEE T-AC, 37: 991–994, 1992.

    Article  MathSciNet  MATH  Google Scholar 

  28. T. Söderström and P. Stoica. System Identification. Prentice-Hall, U.K., 1989.

    MATH  Google Scholar 

  29. P. van den Hof and R. Schrama. An indirect method for transfer function estimation from closed loop data. Automatica, 29 (6), December 1993.

    Google Scholar 

  30. P. van den Hof and R. Schrama. Identification and control — closed-loop issues. Automatica, 31 (12), 1995.

    Google Scholar 

  31. A. Voda-Besançon and I.D. Landau. An iterative method for the auto-calibration of the digital controllers. application. In Proc. ECC95, Rome, Italy, 1995.

    Google Scholar 

  32. Z. Zang, R.R. Bitmead, and M. Gevers. Iterative weighted model refinement and control robustness enhancement. In Proc. 30th IEEE-CDC, Brighton, UK, 1991.

    Google Scholar 

  33. Z. Zang, R.R. Bitmead, and M. Gevers. Iterative weighted least-squares identification and weighted lqg control design. Automatica, 31 (11), 1995.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 1998 Springer-Verlag London Limited

About this chapter

Cite this chapter

Landau, I.D. (1998). Competition, Interaction and Control. In: Normand-Cyrot, D. (eds) Perspectives in Control. Springer, London. https://doi.org/10.1007/978-1-4471-1276-1_21

Download citation

  • DOI: https://doi.org/10.1007/978-1-4471-1276-1_21

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-4471-1278-5

  • Online ISBN: 978-1-4471-1276-1

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics