Reference
A. Firooznia, R. Bourdais, and B. De Schutter, "A distributed algorithm to
determine lower and upper bounds in branch and bound for hybrid model
predictive control,"
Proceedings of the 54th IEEE Conference
on Decision and Control, Osaka, Japan, pp. 1736-1741, Dec. 2015.
Abstract
In this work, a class of model predictive control problems with mixed
real-valued and binary control signals is considered. The optimization problem
to be solved is a constrained Mixed Integer Quadratic Programming (MIQP)
problem. The main objective is to derive a distributed algorithm for limiting
the search space in branch and bound approaches by tightening the lower and
upper bounds of objective function. To this aim, a distributed algorithm is
proposed for the convex relaxation of the MIQP problem via dual decomposition.
The effectiveness of the approach is illustrated with a case study.
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BibTeX
@inproceedings{FirDeS:15-024,
author = {Firooznia, Amir and Bourdais, Romain and De Schutter, Bart},
title = {A Distributed Algorithm to Determine Lower and Upper Bounds in
Branch and Bound for Hybrid Model Predictive Control},
booktitle = {Proceedings of the 54th IEEE Conference on Decision and
Control},
address = {Osaka, Japan},
pages = {1736--1741},
month = dec,
year = {2015}
}