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Nonlinear Model Predictive Control pp 381–389Cite as

Parametric Approach to Nonlinear Model Predictive Control

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

Abstract

In model predictive control (MPC), a dynamic optimization problem (DOP) is solved at each sampling instance for a given value of the initial condition. In this work we show how the computational burden induced by the repetitive solving of the DOP for nonlinear systems can be reduced by transforming the unconstrained DOP to a suboptimal DOP with horizon one. The approach is based on solving the stationary Hamilton-Jacobi-Bellman (HJB) equation along a given path while constructing control Lyapunov function (CLF). It is illustrated that for particular cases the problem can be further simplified to a set of differential algebraic equations (DAE) for which an explicit solution can be found without performing optimization.

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Authors and Affiliations

  1. Institute of Information Engineering, Automation and Mathematics, Slovak University of Technology in Bratislava, Radlinského 9, 81237, Bratislava, Slovakia

    M. Herceg, M. Kvasnica & M. Fikar

Authors
  1. M. Herceg
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  2. M. Kvasnica
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  3. M. Fikar
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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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Herceg, M., Kvasnica, M., Fikar, M. (2009). Parametric Approach to Nonlinear Model Predictive Control. 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_31

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  • DOI: https://doi.org/10.1007/978-3-642-01094-1_31

  • 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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