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Development of Explicit Neural Predictive Control Algorithm Using Particle Swarm Optimisation

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Artificial Intelligence and Soft Computing (ICAISC 2013)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7894))

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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.

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Author information

Authors and Affiliations

  1. Institute of Control and Computation Engineering, Warsaw University of Technology, ul. Nowowiejska 15/19, 00-665, Warsaw, Poland

    Maciej Ławryńczuk

Authors
  1. Maciej Ławryńczuk
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Editor information

Editors and Affiliations

  1. Częstochowa University of Technology, Armii Krajowej 36, 42-200, Częstochowa, Poland

    Leszek Rutkowski , Marcin Korytkowski  & Rafał Scherer ,  & 

  2. AGH University of Science and Technology, Michiewicza 30, 30-059, Kraków, Poland

    Ryszard Tadeusiewicz

  3. Department of Electrical Engineering and Computer Sciences, University of California, 94720-1776, Berkeley, CA, USA

    Lotfi A. Zadeh

  4. Electrical and Computer Engineering, University of Louisville, 405 Lutz Hall, 40292, Louisville, KY, USA

    Jacek M. Zurada

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© 2013 Springer-Verlag Berlin Heidelberg

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Ławryńczuk, M. (2013). Development of Explicit Neural Predictive Control Algorithm Using Particle Swarm Optimisation. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2013. Lecture Notes in Computer Science(), vol 7894. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38658-9_12

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  • DOI: https://doi.org/10.1007/978-3-642-38658-9_12

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-38657-2

  • Online ISBN: 978-3-642-38658-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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