“A comparison of implementation strategies for MPC”Authors: Bernt Lie, Marta D. Díez and Tor A. HaugeAffiliation: Telemark University College Reference: 2005, Vol. 26, No. 1, pp. 39-50. |
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Keywords: Model predictive control; Quadratic programming; Comparison of QP solvers
Abstract: Four quadratic programming (QP) formulations of model predictive control (MPC) are compared with regards to ease of formulation, memory requirement, and numerical properties. The comparison is based on two example processes: a paper machine model, and a model of the Tennessee Eastman challenge process; the number of free variables range from 150-1400. Five commercial QP solvers are compared. Preliminary results indicate that dense solvers still are the most efficient, but sparse solvers hold great promise.
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DOI: 10.4173/mic.2005.1.3
DOI forward links to this article:
[1] Bernt Lie David Di Ruscio Rolf Ergon Bjørn Glemmestad Maths Halstensen Finn Haugen Saba Mylvaganam Nils-Olav Skeie Dietmar Winkler, (2009), doi:10.4173/mic.2009.3.4
[2] Gabriele Pannocchia James B. Rawlings Stephen J. Wright, (2007), doi:10.1016/j.automatica.2006.10.019
[3] Daniel Axehill Lieven Vandenberghe Anders Hansson, (2010), doi:10.1016/j.automatica.2010.06.015
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BibTeX:
@article{MIC-2005-1-3,
title={{A comparison of implementation strategies for MPC}},
author={Lie, Bernt and Díez, Marta D. and Hauge, Tor A.},
journal={Modeling, Identification and Control},
volume={26},
number={1},
pages={29--50},
year={2005},
doi={10.4173/mic.2005.1.3},
publisher={Norwegian Society of Automatic Control}
};
