State Observers for Model Predictive Control

  • Petr Chalupa
  • Peter Januška
  • Jakub Novák
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 210)


This paper deals with state observers with respect to their usage in the model predictive control (MPC) based on state space model of the controlled system. In case of immeasurable states a state observer (filter) is used to calculate current states in each control step. The paper is especially focused to finite impulse filters (FIR) as these filters do not require knowledge of initial state - contrary to infinite impulse response (IIR) filters. Several linear filters are tested and compared with proposed filters based on quadratic and linear programming. Different filter lengths (horizons) were tested to investigate filters’ performance. Filters were tested in very noisy conditions to evaluate filter robustness and therefore its usability in real-time deployment. The simulations were carried out using data from a real-time laboratory (Amira DR300 Servo system). All the measurements and simulations were carried out in MATLAB/Simulink environment.


State observer FIR filter IIR filter Model predictive control 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Camacho, E., Bordons, C.: Model predictive control, 2nd edn. Springer, London (2007)CrossRefGoogle Scholar
  2. 2.
    Bobál, V., Böhm, J., Fessl, J., Macháček, J.: Digital Self-tuning Controllers: Algorithms, Implementation and Applications. Springer, London (2005)Google Scholar
  3. 3.
    Kalman, R.E., Bucy, R.S.: A new approach to linear filtering and prediction problems. Transactions of the ASME–Journal of Basic Engineering 82, 35–45 (1960)CrossRefGoogle Scholar
  4. 4.
    Luenberger, D.G.: Introduction to Dynamic Systems, Theory, Models, and Applications. John Wiley & Sons, New York (1979)MATHGoogle Scholar
  5. 5.
    Song, I.Y., Kim, D.Y., Shin, V., Jeon, M.: Receding horizon filtering for discrete-time linear systems with state and observation delays. IET Radar, Sonar 6(4), 263–271 (2012)CrossRefGoogle Scholar
  6. 6.
    Kwon, W., Han, S.: Receding horizon control: model predictive control for state models. Springer, London (2005)Google Scholar
  7. 7.
    DR300: Laboratory Setup Speed Control with Variable Load, Amira, Duisburg (2000)Google Scholar
  8. 8.
    Bobál, V., Chalupa, P., Kubalčík, M., Dostál, P.: Self-Tuning Predictive Control of Nonlinear Servo-Motor. Journal of Electrical Engineering (2010) Google Scholar
  9. 9.
    Maciejowski, J.M.: Predictive control: with constraints, 1st edn. Prentice Hall, Harlow (2002)Google Scholar
  10. 10.
    Goodwin, G.C., de Dona, J., Seron, M.: Constrained control and estimation: an optimisation approach. Springer, London (2005)MATHGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

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

Personalised recommendations