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Neural Networks in Model Predictive Control

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Intelligent Systems for Knowledge Management

Part of the book series: Studies in Computational Intelligence ((SCI,volume 252))

Abstract

This work is concerned with Model Predictive Control (MPC) algorithms in which neural models are used on-line. Model structure selection, training and stability issues are thoroughly discussed. Computationally efficient algorithms are recommended which use on-line linearisation of the neural model and need solving on-line quadratic optimisation tasks. It is demonstrated that they give very good results, comparable to those obtained when nonlinear optimisation is used on-line in MPC. In order to illustrate the effectiveness of discussed approaches, a chemical process is considered. The development of appropriate models for MPC is discussed, the control accuracy and the computational complexity of recommended MPC are shown.

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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

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  1. Maciej Ławryńczuk
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Editors and Affiliations

  1. Institute of Informatics, Wroclaw University of Technology, Str.Wyb.Wypsianskiego 27, 50-370, Wroclaw, Poland

    Ngoc Thanh Nguyen

  2. Faculty of Management and Economics, Gdansk University of Technology, Str.Narutowicza 11/12, 80-233, Gdansk, Poland

    Edward Szczerbicki

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Ławryńczuk, M. (2009). Neural Networks in Model Predictive Control. In: Nguyen, N.T., Szczerbicki, E. (eds) Intelligent Systems for Knowledge Management. Studies in Computational Intelligence, vol 252. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04170-9_2

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  • DOI: https://doi.org/10.1007/978-3-642-04170-9_2

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-04169-3

  • Online ISBN: 978-3-642-04170-9

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