Skip to main content
SpringerLink
Log in

Nonlinear Model Predictive Control Using Neural Net Plant Models

  • Chapter
Methods of Model Based Process Control

Part of the book series: NATO ASI Series ((NSSE,volume 293))

Abstract

An application of neural net plant models to nonlinear model predictive control is presented. Feedforward neural nets are trained with input / output data from a plant under conventional control to obtain a black-box-model of the plant. This model is used for the prediction task in an extended DMC scheme. Experimental results for a SISO-example (continuous neutralization reactor) and a MIMO-example (level control) show a significant improvement of the controller performance compared to the conventional controllers used for generating the training data.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. K. Hornik, M. Stincombe, H. White: Universal Approximation of an Unknown Mapping and Its Derivatives Using Multilayer Feedforward Networks. Neural Networks 3, 211–223, 1990.

    Article  Google Scholar 

  2. D. E. Rumelhardt, G. E. Hinton, R. J. Williams: Learning Internal Representations by Error Propagation. Parallel Distributed Processing 1, 45–76, MIT Press, 1986.

    Google Scholar 

  3. T. Tollenaere: SuperSAB: Fast Adaptive Backpropagation with Good Scaling Properties. Neural Networks 3, 561–573, 1990.

    Article  Google Scholar 

  4. T. Peterson, E. Hernandez, Y. Arkun, F. J. Schork: Nonlinear Predictive Control of a Semi Batch Polymerisation Reactor by an Extended DMC. Proc. 1989 American Control Conference, 1534–1539, 1989.

    Google Scholar 

  5. C. Cutler, B. Ramaker: Dynamic Matrix Control — A Computer Control Algorithm, Proc. 1980 Joint Automatic Control Conference.

    Google Scholar 

  6. H. Ranke: Modellbasierte prädiktive Regelung eines Neutralisationsreaktors mit Neuronalen Netzen, Diplomarbeit, Universität Dortmund, Fachbereich Chemietechnik. 1993.

    Google Scholar 

  7. U. Meier: Mathematische Modellierung und Entwurf eines nichtlinearen Reglers für einen kontinuierlich betriebenen Neutralisationsreaktor, Diplomarbeit, Universität Dortmund, Fachbereich Chemietechnik, 1993.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Lehrstuhl für Anlagensteuerungstechnik (Process Control Group) Department of Chemical Engineering, University of Dortmund, D-44221, Dortmund, Germany

    A. Draeger & S. Engell

Authors
  1. A. Draeger
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. S. Engell
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Chemical Engineering, Ankara University, Ankara, Turkey

    Ridvan Berber

Rights and permissions

Reprints and permissions

Copyright information

© 1995 Springer Science+Business Media Dordrecht

About this chapter

Cite this chapter

Draeger, A., Engell, S. (1995). Nonlinear Model Predictive Control Using Neural Net Plant Models. In: Berber, R. (eds) Methods of Model Based Process Control. NATO ASI Series, vol 293. Springer, Dordrecht. https://doi.org/10.1007/978-94-011-0135-6_23

Download citation

  • DOI: https://doi.org/10.1007/978-94-011-0135-6_23

  • Publisher Name: Springer, Dordrecht

  • Print ISBN: 978-94-010-4061-7

  • Online ISBN: 978-94-011-0135-6

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation