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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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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