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Predictive Control of a Distillation Column Using a Control-Oriented Neural Model

  • Maciej Ławryńczuk
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6593)

Abstract

This paper describes a special neural model developed with the specific aim of being used in nonlinear Model Predictive Control (MPC). The model consists of two neural networks. The model structure strictly mirrors its role in a suboptimal (linearisation-based) MPC algorithm: the first network is used to calculate on-line the influence of the past, the second network directly estimates the time-varying step-response of the locally linearised neural model, without explicit on-line linearisation. Advantages of MPC based on the described model structure (high control accuracy, computational efficiency and easiness of development) are demonstrated in the control system of a distillation column.

Keywords

Process control Model Predictive Control neural networks optimisation soft computing 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

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

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