Advertisement

Predictor-Based Self Tuning Control with Constraints

  • Vytautas Kaminskas
Part of the Optimization and Its Applications book series (SOIA, volume 4)

Summary

Design problems of predictor-based self tuning digital control systems for different types of linear and non-linear dynamical plants are discussed. Control systems based on generalized minimum variance algorithms with amplitude and introduction rate restrictions for the control signal are considered in the article.

Key words

predictor-based self tuning control generalized minimum variance control constraints for the control signal 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. [AW84]
    Aström, K.J., Wittenmark, B.: Computer Controlled Systems — Theory and Design. Prentice Hall, Englewood Cliffs (1984)Google Scholar
  2. [CB95]
    Camacho, E.F., Bordons, C: Model Predictive Control in the Process Industry. Springer, London (1995)Google Scholar
  3. [CB98]
    Camacho, E.F., Bordons, C: Model Predictive Control. Springer, Berlin (1998)Google Scholar
  4. [Cla84]
    Clarke, D.W.: Self-tuning control of non minimum-phase systems. Automatica, 20(5), 501–517 (1984)zbMATHCrossRefMathSciNetGoogle Scholar
  5. [Cla94]
    Clarke, D.W.: Advances in Model Predictive Control. Oxford Science Publications, Oxford, UK (1994)zbMATHGoogle Scholar
  6. [CMT87]
    Clarke, D.W., Mohtadi, C, Tuffs, P.S.: Generalized predictive control. Automatica, 23(2), 137–160 (1987)zbMATHCrossRefGoogle Scholar
  7. [EV98]
    Espinosa, J., Vandewalle, J.: Predictive control using fuzzy models applied to a steam generating unit. In: Ruan, D., Abderrahim, H.A., D’hondt, P., Kerre, E. (eds) Fuzzy Logic and Intelligent Technologies for Nuclear Science and Industry, Proceedings of the Third International FLINS Workshop, Vol. 3 of Fuzzy Logic and Intelligent Technologies for Nuclear Science and Industry. World Scientific, Singapore, 151–160 (1998)Google Scholar
  8. [Ise81]
    Isermann, R,.: Digital Control Systems. Springer, Berlin (1981)zbMATHGoogle Scholar
  9. [JK98]
    Juang, J.N., Kenneth, W. E.: Predictive Feedback and Feedforward Control for Systems with Unknown Disturbances. National Aeronautics and Space Administration, Langley Research Center, Hampton, Virginia (1998)Google Scholar
  10. [Kam82]
    Kaminskas, V.: Dynamic System Identification via Discrete-time Observations. Pt. I. Statistical Method Foundations. Estimation in Linear Systems. Mokslas, Vilnius (1982)Google Scholar
  11. [Kam88]
    Kaminskas, V.: Predictor-based self-tuning control systems. In: 33 Intern. Wiss. Koll, TH Ilmenau, Heft 1, 153–156 (1988)Google Scholar
  12. [KJV90]
    Kaminskas, V., Janickienė, D., Vitkutė, D.: Self-tuning control of the nuclear reactor power. In: Preprints of 11th World Congress “Automatic Control in the Service of Mankind”, Tallin, Vol. 11, 91–96 (1990)Google Scholar
  13. [KJV92]
    Kaminskas, V., Janickienė, D., Vitkutė, D.: Self-turning control of a power plant. In: preprints of IFAC Symp. on Control of Power Plants and Power Systems, Munich, Vol. 1, 229–234 (1992)Google Scholar
  14. [KŠT91]
    Kaminskas, V., Šidlauskas, K., Tallat-Kelpsa, C: Constrained self-tuning control of stochastic extremal systems. Informatica, 2(1), 33–52 (1991)MathSciNetGoogle Scholar
  15. [KTŠ88]
    Kaminskas, V., Tallat-Kelpsa, C, Šidlauskas, K.: Self-tuning minimum-variance control of nonlinear Wiener-Hammerstein-type systems. In: Preprints 8th IFAC/IFORS Symp. on Ident. and Syst. Par. Est., Beijing, Vol. 1, 384–389 (1988)Google Scholar
  16. [ML99]
    Morari, M., Lee, J.H.: Model predictive control: past, present and future. Computers and Chemical Engineering, 23, 667–682 (1999)CrossRefGoogle Scholar
  17. [MS05]
    Murray-Smith, R., Sbarbaro, D.: Self-tuning control of non-linear systems using Gaussian process prior models. Switching and Learning. LNCS 3355, Springer, Berlin, 140–157 (2005)Google Scholar
  18. [MSRG03]
    Murray-Smith, R., Sbarbaro, D., Rasmussen, C.E., Girard, A.: Adaptive, Cautious, Predictive Control with Gaussian Process Priors, 13th IFAC Symposium on System Identification, IFAC, Rotterdam (2003)Google Scholar
  19. [Pet84]
    Peterka, V.: Predictor-based self-tuning control. Automatica, 19(5), 471–486 (1984)Google Scholar
  20. [Ric93]
    Richalet, J.: Industrial applications of model based predictive control. Automatica, 23(5), (1993)Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2007

Authors and Affiliations

  • Vytautas Kaminskas
    • 1
  1. 1.Department of Applied InformaticsVytautas Magnus UniversityKaunasLithuania

Personalised recommendations