An Efficient Nonlinear Predictive Control Algorithm with Neural Models and Its Application to a High-Purity Distillation Process

  • Maciej Ławryńczuk
  • Piotr Tatjewski
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4029)


This paper is concerned with a computationally efficient (suboptimal) nonlinear model-based predictive control (MPC) algorithm and its application to a high-purity high-pressure ethylene-ethane distillation column. A neural model of the process is used on-line to determine the local linearisation and the nonlinear free response. In comparison with general nonlinear MPC technique, which hinges on non-convex optimisation, the presented structure is far more reliable and less computationally demanding because it results in a quadratic programming problem, whereas its closed-loop control performance is similar.


Model Predictive Control Feedforward Neural Network Neural Model Quadratic Programming Problem Prediction Horizon 


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  1. 1.
    Haykin, S.: Neural networks – a comprehensive foundation. Prentice-Hall, Englewood Cliffs (1999)MATHGoogle Scholar
  2. 2.
    Henson, M.A.: Nonlinear model predictive control: current status and future directions. Computers and Chemical Engineering 23, 187–202 (1998)CrossRefGoogle Scholar
  3. 3.
    Liu, G.P., Kadirkamanathan, V., Billings, S.A.: Predictive control for non-linear systems using neural networks. International Journal of Control 71, 1119–1132 (1998)MATHCrossRefMathSciNetGoogle Scholar
  4. 4.
    Ławryńczuk, M., Tatjewski, P.: A stable dual-mode type nonlinear predictive control algorithm based on on-line linearisation and quadratic programming. In: Proceedings of the 10th International Conference on Methods and Models in Automation and Robotics, Miedzyzdroje, Poland, pp. 503–510 (2004)Google Scholar
  5. 5.
    Ławryńczuk, M.: Nonlinear model predictive control algorithms with neural models of processes (in Polish). PhD thesis. Warsaw University of Technology (2003)Google Scholar
  6. 6.
    Ławryńczuk, M., Tatjewski, P.: A computationally efficient nonlinear predictive control algorithm based on neural models. In: Proceedings of the 8th International Conference on Methods and Models in Automation and Robotics, Szczecin, Poland, pp. 781–786 (2002)Google Scholar
  7. 7.
    Maciejowski, J.M.: Predictive control with constraints. Prentice-Hall, Englewood Cliffs (2002)Google Scholar
  8. 8.
    Mahfouf, M., Linkens, D.A.: Non-linear generalized predictive control (NLGPC) applied to muscle relaxant anaesthesia. International Journal of Control 71, 239–257 (1998)MATHCrossRefMathSciNetGoogle Scholar
  9. 9.
    Michalska, H., Mayne, D.Q.: Robust receding horizon control of constrained nonlinear systems. IEEE Transactions on Automatic Control 38, 1623–1633 (1993)MATHCrossRefMathSciNetGoogle Scholar
  10. 10.
    Morari, M., Lee, J.H.: Model predictive control: past, present and future. Computers and Chemical Engineering 23, 667–682 (1999)CrossRefGoogle Scholar
  11. 11.
    Parisini, T., Sanguineti, M., Zoppoli, R.: Nonlinear stabilization by receding-horizon neural regulators. International Journal of Control 70, 341–362 (1998)MATHCrossRefMathSciNetGoogle Scholar
  12. 12.
    Piche, S., Sayyar-Rodsari, B., Johnson, D., Gerules, M.: Nonlinear model predictive control using neural networks. IEEE Control Systems Magazine 20, 56–62 (2000)CrossRefGoogle Scholar
  13. 13.
    Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11, 733–764 (2003)CrossRefGoogle Scholar
  14. 14.
    Tatjewski, P., Ławryńczuk, M.: Soft computing in model-based predictive control. International Journal of Applied Mathematics and Computer Science 16, 101–120 (2006)MathSciNetGoogle Scholar
  15. 15.
    Tatjewski, P.: Advanced Control of Industrial Processes. Structures and Algorithms (in Polish). EXIT Academic Publishing House. Warsaw (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Maciej Ławryńczuk
    • 1
  • Piotr Tatjewski
    • 1
  1. 1.Institute of Control and Computation EngineeringWarsaw University of TechnologyWarsawPoland

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