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Relaxations Applicable to Mixed Integer Predictive Control - Comparisons and Efficient Computations
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
University of California, LA, USA.
2008 (English)Report (Other academic)
Abstract [en]

In this work, different relaxations applicable to an MPC problem with a mix of real valued and binary valued control signals are compared. In the problem description considered, there are linear inequality constraints on states and control signals. The relaxations are related theoretically and both the tightness of the bounds and the computational complexities are compared in numerical experiments. The relaxations considered are the quadratic programming (QP) relaxation, the standard semidefinite programming (SDP) relaxation and an equality constrained SDP relaxation. The result is that the standard SDP relaxation is the one that usually gives the best bound and is most computationally demanding, while the QP relaxation is the one that gives the worst bound and is least computationally demanding. The equality constrained relaxation presented in this paper often gives a better bound than the QP relaxation and is less computationally demanding compared to the standard SDP relaxation. Furthermore, it is also shown how the equality constrained SDP relaxation can be efficiently computed by solving the Newton system in an Interior Point algorithm using a Riccati recursion. This makes it possible to compute the equality constrained relaxation with approximately linear computational complexity in the prediction horizon.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2008. , 9 p.4103-4109 p.
LiTH-ISY-R, ISSN 1400-3902 ; 2839
Keyword [en]
Newton method, Riccati equations, Computational complexity, Predictive control, Quadratic programming, Relaxation theory, Interior point algorithm, Newton system, Riccati recursion, Linear computational complexity, Linear inequality constraints, Mixed integer predictive control
National Category
Control Engineering
URN: urn:nbn:se:liu:diva-56158ISRN: LiTH-ISY-R-2839OAI: diva2:316943
Available from: 2010-04-30 Created: 2010-04-30 Last updated: 2014-06-19Bibliographically approved

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Axehill, DanielHansson, Anders
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