Reference
S. S. Farahani, T. van den Boom, and B. De
Schutter, "Model predictive control for stochastic max-min-plus-scaling systems
- An approximation approach,"
Proceedings of the 2011 50th
IEEE Conference on Decision and Control and European Control Conference
(CDC-ECC), Orlando, Florida, pp. 391-396, Dec. 2011.
Abstract
A large class of discrete-event and hybrid systems can be described by a
max-min-plus-scaling (MMPS) model, i.e., a model in which the main operations
are maximization, minimization, addition, and scalar multiplication. Further,
Model Predictive Control (MPC), which is one of the most widely used advanced
control design methods in the process industry due to its ability to handle
constraints on both inputs and outputs, has already been extended to both
deterministic and stochastic MMPS systems. However, in order to compute an MPC
controller for a general MMPS system, a nonlinear, nonconvex optimization
problem has to be solved. In addition, for stochastic MMPS systems, the problem
is computationally highly complex since the cost function is defined as the
expected value of an MMPS function and its evaluation leads to a complex
numerical integration. The aim of this paper is to decrease this computational
complexity by applying an approximation method that is based on the raw moments
of a random variable, to a stochastic MMPS system with a Gaussian noise. In
this way, the problem can be transformed into a sequence of convex optimization
problems, providing that linear or convex MPC input constraints are considered.
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BibTeX
@inproceedings{Farvan:11-040,
author = {Farahani, Samira S. and van den Boom, Ton and De Schutter,
Bart},
title = {Model Predictive Control for Stochastic Max-Min-Plus-Scaling
Systems -- {An} Approximation Approach},
booktitle = {Proceedings of the 2011 50th IEEE Conference on Decision and
Control and European Control Conference (CDC-ECC)},
address = {Orlando, Florida},
pages = {391--396},
month = dec,
year = {2011}
}