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Methods of Model Based Process Control pp 367–396Cite as

Optimization in Model Predictive Control

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Part of the book series: NATO ASI Series ((NSSE,volume 293))

Abstract

This paper explores the interaction between model predictive control and optimization. The success of model predictive control in controlling constrained linear systems is due, in large part, to the fact that the online optimization problem is convex, usually a quadratic programme, for which reliable software is available. If the system is nonlinear, a more complex optimization, whose success is not guaranteed, is required. It is therefore important to modify, if possible, the online optimization problem to facilitate its solution, while maintaining the desirable properties of model predictive control. The paper shows how this may be done using, inter alia, a variable horizon. Secondly it examines how the structure of the optimal control problem impacts on the choice of optimization algorithm.

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Author information

Authors and Affiliations

  1. Department of Electrical and Computer Engineering, University of California, Davis, California, CA, 95616, USA

    D. Q. Mayne

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  1. D. Q. Mayne
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Editor information

Editors and Affiliations

  1. Department of Chemical Engineering, Ankara University, Ankara, Turkey

    Ridvan Berber

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© 1995 Springer Science+Business Media Dordrecht

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Mayne, D.Q. (1995). Optimization in Model Predictive Control. In: Berber, R. (eds) Methods of Model Based Process Control. NATO ASI Series, vol 293. Springer, Dordrecht. https://doi.org/10.1007/978-94-011-0135-6_15

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  • DOI: https://doi.org/10.1007/978-94-011-0135-6_15

  • Publisher Name: Springer, Dordrecht

  • Print ISBN: 978-94-010-4061-7

  • Online ISBN: 978-94-011-0135-6

  • eBook Packages: Springer Book Archive

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