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Practical Issues in Nonlinear Model Predictive Control: Real-Time Optimization and Systematic Tuning

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Nonlinear Model Predictive Control

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

Abstract

In this paper, we discuss two important practical issues in nonlinear model predictive control (NMPC): real-time optimization and systematic tuning. First, we present a couple of efficient algorithms based on the assumption that the sampling period is sufficiently short. Real-time algorithms are obtained in a unified manner as initial-value problems of ordinary differential equations (ODEs) for unknown quantities. A brief survey is given on applications of such ODE-type real-time algorithms in mechanical systems. Furthermore, as a first step toward systematic tuning of a performance index, we propose combining feedback linearization with NMPC. The proposed performance index can be tuned with only one parameter to adjust the output response and the magnitude of the control input. The effectiveness of the proposed method is demonstrated in numerical examples.

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Author information

Authors and Affiliations

  1. Department of Systems Innovation, Graduate School of Engineering Science, Osaka University,  

    Toshiyuki Ohtsuka

  2. Department of Mechanical Engineering, Graduate School of Engineering, Osaka University,  

    Kohei Ozaki

Authors
  1. Toshiyuki Ohtsuka
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  2. Kohei Ozaki
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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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Ohtsuka, T., Ozaki, K. (2009). Practical Issues in Nonlinear Model Predictive Control: Real-Time Optimization and Systematic Tuning. 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_35

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  • DOI: https://doi.org/10.1007/978-3-642-01094-1_35

  • 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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