Adaptive Predictive Control of Time-Delay Systems

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 192)


Design of an optimal controller for higher-order systems often leads to complex control algorithms. One of the possibilities of control of such processes is their approximation by a lower-order model with a time-delay (dead time). These time-delay processes can be effectively handled by model-based predictive control (MPC) method. The paper deals with design of an algorithm for adaptive predictive control of higher-order processes which are approximated by a second-order model of the process with a time-delay. The controller was tested and verified by simulation on experimental data from a laboratory model of a heat exchanger.


Heat Exchanger Model Predictive Control Predictive Controller Flow Heater Predictive Control Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Camacho, E.F., Bordons, C.: Model Predictive Control. Springer, London (2004)zbMATHCrossRefGoogle Scholar
  2. 2.
    Bobál, V., Chalupa Petr, P., Kubalčík, M., Dostál, P.: Self-tuning Predictive Control of Non-linear Servo-motor. Journal of Electrical Engineering 61, 365–372 (2010)CrossRefGoogle Scholar
  3. 3.
    Mikleš, J., Fikar, M.: Process Modelling, Optimisation and Control. Springer, Berlín (2008)Google Scholar
  4. 4.
    Kwon, W.H., Choj, H., Byun, D.G., Noh, S.: Recursive solution of generalized predictive control and its equivalence to receding horizon tracking control. Automatica 28, 1235–1238 (1992)zbMATHCrossRefGoogle Scholar
  5. 5.
    Rossiter, J.A.: Model Based Predictive Control: a Practical Approach. CRC Press (2003)Google Scholar
  6. 6.
    Bobál, V., Chalupa, P., Dostál, P., Kubalčík, M.: Design and simulation verification of self-tuning Smith Predictors. International Journal of Mathematics and Computers in Simulation 5, 342–351 (2011)Google Scholar
  7. 7.
    Lagarias, J.C., Reeds, J.A., Wright, M.H., Wright, P.E.: Convergence properties of the Nelder-Mead Simplex Method in Low Dimensions. SIAM Journal of Optimization 9, 112–147 (1998)MathSciNetzbMATHCrossRefGoogle Scholar
  8. 8.
    Kulhavý, R.: Restricted exponential forgetting in real time identification. Automatica 23, 586–600 (1987)CrossRefGoogle Scholar
  9. 9.
    Bobál, V., Böhm, J., Fessl, J., Macháček, J.: Digital Self-tuning Controllers: Algorithms, Implementation and Applications. Springer, London (2005)Google Scholar
  10. 10.
    Pekař, L., Prokop, R., Dostálek, P.: An anisochronic model of a laboratory heating system. In: Proceedings of 13th WSEAS International Conference on Systems, Rhodes, Greece, pp. 165–172 (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Faculty of Applied InformaticsTomas Bata University in ZlínZlinCzech Republic

Personalised recommendations