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Nonlinear Model Predictive Control: An Introductory Review

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Assessment and Future Directions of Nonlinear Model Predictive Control

Part of the book series: Lecture Notes in Control and Information Sciences ((LNCIS,volume 358))

Abstract

Model Predictive Control (MPC) originated in the late seventies and has developed considerably since then. The term Model Predictive Control does not designate a specific control strategy but rather an ample range of control methods which make explicit use of a model of the process to obtain the control signal by minimizing an objective function. The ideas, appearing in greater or lesser degree in the predictive control family, are basically the explicit use of a model to predict the process output at future time instants (horizon), the calculation of a control sequence minimizing an objective function and the use of a receding strategy, so that at each instant the horizon is displaced towards the future, which involves the application of the first control signal of the sequence calculated at each step.

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Author information

Authors and Affiliations

  1. Dept. de Ingenieria de Sistemas y Automatica, University of Seville, Spain

    Eduardo F. Camacho & Carlos Bordons

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  1. Eduardo F. Camacho
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  2. Carlos Bordons
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Editors and Affiliations

  1. Institute for Systems Theory and Automatic Control, University of Stuttgart, Pfaffenwaldring 9, 70550, Stuttgart, Germany

    Rolf Findeisen  & Frank Allgöwer  & 

  2. Chemical Engineering Department, Carnegie Mellon University, Pittsburgh, PA, 15213, USA

    Lorenz T. Biegler

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Camacho, E.F., Bordons, C. (2007). Nonlinear Model Predictive Control: An Introductory Review. In: Findeisen, R., Allgöwer, F., Biegler, L.T. (eds) Assessment and Future Directions of Nonlinear Model Predictive Control. Lecture Notes in Control and Information Sciences, vol 358. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72699-9_1

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  • DOI: https://doi.org/10.1007/978-3-540-72699-9_1

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-72698-2

  • Online ISBN: 978-3-540-72699-9

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