Reference
S. S. Farahani, T. van den Boom, H. van
der Weide, and B. De Schutter, "An approximation approach for model predictive
control of stochastic max-plus linear systems,"
Proceedings of
the 10th International Workshop on Discrete Event Systems (WODES 2010),
Berlin, Germany, pp. 376-381, Aug.-Sept. 2010.
Abstract
Model Predictive Control (MPC) is a model-based control method based on a
receding horizon approach and online optimization. In previous work we have
extended MPC to a class of discrete-event systems, namely the max-plus linear
systems, i.e., models that are "linear" in the max-plus algebra. Lately, the
application of MPC for stochastic max-plus-linear systems has attracted a lot
of attention. At each event step, an optimization problem then has to be solved
that is, in general, a highly complex and computationally hard problem.
Therefore, the focus of this paper is on decreasing the computational
complexity of the optimization problem. To this end, we use an approximation
approach that is based on the p-th raw moments of a random variable. This
method results in a much lower computational complexity and computation time
while still guaranteeing a good performance.
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BibTeX
@inproceedings{Farvan:10-039,
author = {Farahani, Samira S. and van den Boom, Ton and van der Weide,
Hans and De Schutter, Bart},
title = {An Approximation Approach for Model Predictive Control of
Stochastic Max-Plus Linear Systems},
booktitle = {Proceedings of the 10th International Workshop on Discrete
Event Systems (WODES 2010)},
address = {Berlin, Germany},
pages = {376--381},
month = aug # {--} # sep,
year = {2010}
}