Primary Method of Quadratic Programming in Multivariable Predictive Control with Constraints

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 289)


General Predictive Control (GPC) is a modern method for process control which is appropriate for many characters of processes. In this paper there is proposed possibility of optimization, which is performed in each sampling period, in the GPC algorithm. Lower time of calculations is in general important for GPC control of multivariable systems with many constraints. An improvement of a primary method of quadratic programming task is proposed in this paper. Computational time can be reduced by changes in details of the optimization method. Time reserves are analyzed in a case of a nonlinear constrained problem which is represented by the Active Set Method. The improved method is presented and results are discussed in simulations.


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  1. 1.
    Camacho, E.F., Bordons, C.: Model Predictive Control. Springer, London (2007)CrossRefGoogle Scholar
  2. 2.
    Rawlings, J.B., Mayne, D.Q.: Model Predictive Control Theory and Design. Nob Hill Pub. (2009)Google Scholar
  3. 3.
    Kubalčík, M., Bobál, V.: Computation of predictions in multivariable predictive control. In: Proceedings of the 13th WSEAS International Conference on Automatic Control, Modelling & Simulation, ACMOS 2011 (2011)Google Scholar
  4. 4.
    Bobal, V., Kubalcik, M., Dostal, P., Matejicek, J.: Adaptive predictive control of time-delay systems. Computers & Mathematics with Applications 66(2), 165–176 (2013)CrossRefMathSciNetGoogle Scholar
  5. 5.
    Kučera, V.: Analysis and Design of Discrete Linear Control Systems. Nakladatelství Československé akademie věd, Praha (1991)Google Scholar
  6. 6.
    Wang, L.: Model Predictive Control System Design and Implementation Using MATLAB. Springer-Verlag Limited, London (2009)MATHGoogle Scholar
  7. 7.
    Kwon, W.H.: Receding horizon control: model predictive control for state models. Springer, London (2005)Google Scholar
  8. 8.
    Corriou, J.-P.: Process control: theory and applications. Springer, London (2004)CrossRefGoogle Scholar
  9. 9.
    Lee, G.M., Tam, N.N., Yen, N.D.: Quadratic Programming and Affine Variational Inequalities: A Qualitative Study. Springer (2005)Google Scholar
  10. 10.
    Luenberger, D.G., Ye, Y.: Linear and nonlinear programming, 3rd edn. Springer, New York (2008)MATHGoogle Scholar
  11. 11.
    Dostál, Z.: Optimal Quadratic Programming Algorithms: With Applications to Variational Inequalities. Springer, New York (2009)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Department of Process Control, Faculty of Applied InformaticsTomas Bata University in ZlínZlínCzech Republic

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