Development of Explicit Neural Predictive Control Algorithm Using Particle Swarm Optimisation

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


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.


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


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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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