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Nonlinear Model Predictive Control pp 315–323Cite as

MPC for Tracking of Constrained Nonlinear Systems

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Part of the book series: Lecture Notes in Control and Information Sciences ((LNCIS,volume 384))

Abstract

This paper deals with the tracking problem for constrained nonlinear systems using a model predictive control (MPC) law. MPC provides a control law suitable for regulating constrained linear and nonlinear systems to a given target steady state. However, when the target operating point changes, the feasibility of the controller may be lost and the controller fails to track the reference. In this paper, a novel MPC for tracking changing constant references is presented. This controller extend a recently presented MPC for tracking for constrained linear systems to the nonlinear case. The main characteristics of this controller are: considering an artificial steady state as a decision variable, minimizing a cost that penalizes the error with the artificial steady state, adding to the cost function an additional term that penalizes the deviation between the artificial steady state and the target steady state (the so-called offset cost function) and considering an invariant set for tracking as extended terminal constraint. The properties of this controller has been tested on a constrained CSTR simulation model.

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Author information

Authors and Affiliations

  1. Departamento de Ingeniería de Sistemas y Automática., Universidad de Sevilla, Escuela Superior de Ingenieros., Camino de los Descubrimientos s/n., 41092, Sevilla, Spain

    D. Limon, A. Ferramosca, I. Alvarado, T. Alamo & E. F. Camacho

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  1. D. Limon
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  2. A. Ferramosca
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  3. I. Alvarado
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  4. T. Alamo
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  5. E. F. Camacho
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Editor information

Editors and Affiliations

  1. Dipartimento di Informatica e Sistemistica, Universita’ di Pavia, via Ferrata 1, 27100 Pavia, Italy

    Lalo Magni

  2. Automatic Control Laboratory, ETH Zurich , ETL I 24.2, Physikstrasse 3, CH-8092, Zürich, Switzerland

    Davide Martino Raimondo

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

    Frank Allgöwer

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© 2009 Springer-Verlag Berlin Heidelberg

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Limon, D., Ferramosca, A., Alvarado, I., Alamo, T., Camacho, E.F. (2009). MPC for Tracking of Constrained Nonlinear Systems. In: Magni, L., Raimondo, D.M., Allgöwer, F. (eds) Nonlinear Model Predictive Control. Lecture Notes in Control and Information Sciences, vol 384. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01094-1_26

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  • DOI: https://doi.org/10.1007/978-3-642-01094-1_26

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-01093-4

  • Online ISBN: 978-3-642-01094-1

  • eBook Packages: EngineeringEngineering (R0)

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