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Development of Explicit Neural Predictive Control Algorithm Using Particle Swarm Optimisation

  • Maciej Ławryńczuk
Conference paper
  • 1.3k Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7894)

Abstract

This paper describes development of a nonlinear Model Predictive Control (MPC) algorithm. The algorithm is very computationally efficient because for control signal calculation an explicit control law is used, no on-line optimisation is necessary. The control law is implemented by a neural network which is trained off-line by means of a particle swarm optimisation algorithm. Inefficiency of a classical gradient-based training algorithm is demonstrated for the polymerisation reactor. Moreover, the discussed MPC algorithm is compared in terms of accuracy and computational complexity with two suboptimal MPC algorithms with model linearisation and MPC with full nonlinear optimisation.

Keywords

Process control Model Predictive Control neural networks optimisation particle swarm optimisation soft computing 

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References

  1. 1.
    Eberhart, R.C., Shi, Y., Kennedy, J.: Swarm Intelligence. Morgan Kaufmann (2001)Google Scholar
  2. 2.
    Haykin, S.: Neural networks–a comprehensive foundation. Prentice Hall, Englewood Cliffs (1999)zbMATHGoogle Scholar
  3. 3.
    Henson, M.A.: Nonlinear model predictive control: current status and future directions. Computers and Chemical Engineering 23, 187–202 (1998)CrossRefGoogle Scholar
  4. 4.
    Ławryńczuk, M.: Explicit neural network-based nonlinear predictive control with low computational complexity. In: Szczuka, M., Kryszkiewicz, M., Ramanna, S., Jensen, R., Hu, Q. (eds.) RSCTC 2010. LNCS (LNAI), vol. 6086, pp. 649–658. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  5. 5.
    Ławryńczuk, M.: Neural networks in model predictive control. In: Nguyen, N.T., Szczerbicki, E. (eds.) Intelligent Systems for Knowledge Management. SCI, vol. 252, pp. 31–63. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  6. 6.
    Ławryńczuk, M.: Explicit nonlinear predictive control of a distillation column based on neural models. Chemical Engineering and Technology 32, 1578–1587 (2009)CrossRefGoogle Scholar
  7. 7.
    Ławryńczuk, M.: A family of model predictive control algorithms with artificial neural networks. International Journal of Applied Mathematics and Computer Science 17, 217–232 (2007)MathSciNetzbMATHGoogle Scholar
  8. 8.
    Maciejowski, J.M.: Predictive control with constraints. Prentice Hall, Harlow (2002)Google Scholar
  9. 9.
    Maner, B.R., Doyle, F.J., Ogunnaike, B.A., Pearson, R.K.: Nonlinear model predictive control of a simulated multivariable polymerization reactor using second-order Volterra models. Automatica 32, 1285–1301 (1996)MathSciNetzbMATHCrossRefGoogle 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.
    Nørgaard, M., Ravn, O., Poulsen, N.K., Hansen, L.K.: Neural networks for modelling and control of dynamic systems. Springer, London (2000)CrossRefGoogle Scholar
  12. 12.
    Pillay, N., Govender, P.: Particle swarm optimization of PID tuning paremeters: optimal tuning of single-input-single-output control loops. LAP Lambert Academic Publishing (2010)Google Scholar
  13. 13.
    Pourjafari, E., Mojallali: Predictive control for voltage collapse avoidance using a modified discrete multi-valued PSO algorithm. ISA Transactions 50, 195–200 (2011)CrossRefGoogle Scholar
  14. 14.
    Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11, 733–764 (2003)CrossRefGoogle Scholar
  15. 15.
    Tatjewski, P.: Advanced control of industrial processes, Structures and algorithms. Springer, London (2007)zbMATHGoogle Scholar
  16. 16.
    Yousuf, M.S.: Nonlinear predictive control using particle swarm optimization: application to power systems. VDM Verlag (2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

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

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