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Stability, Feasibility, Optimality and the Degrees of Freedom in Constrained Predictive Control

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Nonlinear Model Predictive Control

Part of the book series: Progress in Systems and Control Theory ((PSCT,volume 26))

Abstract

The characterization of the class of stabilizing predictions for linear systems allows for predictive control algorithms which interpolate linearly between some key predicted input trajectories. Such interpolation endows the closed-loop system with desirable attributes even when a small number of degrees of freedom is used. This paper explores the ideas behind interpolation and extends them to the case of predictive control of nonlinear systems by proposing an effective yet computationally undemanding strategy.

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Author information

Authors and Affiliations

  1. Department of Eng. Science, University of Oxford, Parks Road, Oxford, OXl 3PJ, UK

    Basil Kouvaritakis, Mark Cannon & J. Anthony Rossiter

Authors
  1. Basil Kouvaritakis
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  2. Mark Cannon
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  3. J. Anthony Rossiter
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Editor information

Editors and Affiliations

  1. Institut für Systemtheorie technischer Prozesse, Universität Stuttgart, Pfaffenwaldring 9, Stuttgart, 70550, Germany

    Frank Allgöwer

  2. Department of Chemical Engineering, University of Massachusetts at Amherst, 159 Goessmann Lab, Amherst, 01003-3110, MA, USA

    Alex Zheng

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© 2000 Springer Basel AG

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Kouvaritakis, B., Cannon, M., Rossiter, J.A. (2000). Stability, Feasibility, Optimality and the Degrees of Freedom in Constrained Predictive Control. In: Allgöwer, F., Zheng, A. (eds) Nonlinear Model Predictive Control. Progress in Systems and Control Theory, vol 26. Birkhäuser, Basel. https://doi.org/10.1007/978-3-0348-8407-5_5

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  • DOI: https://doi.org/10.1007/978-3-0348-8407-5_5

  • Publisher Name: Birkhäuser, Basel

  • Print ISBN: 978-3-0348-9554-5

  • Online ISBN: 978-3-0348-8407-5

  • eBook Packages: Springer Book Archive

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