**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.

Corresponding technical report: pdf file (152 KB)

@inproceedings{Farvan:11-040,

author={S.S. Farahani and T. van den Boom and B. {D}e Schutter},

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}

}

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