**Reference:**

B. De Schutter and
T. van den Boom,
"Model predictive control for max-min-plus systems," in *Discrete
Event Systems: Analysis and Control *(Proceedings of the 5th
International Workshop on Discrete Event Systems (WODES2000), Ghent,
Belgium, Aug. 2000) (R. Boel and G. Stremersch, eds.), vol. 569 of
*The Kluwer International Series in Engineering and Computer
Science*, Boston, Massachusetts: Kluwer Academic Publishers, ISBN
0-7923-7897-0, pp. 201-208, 2000.

**Abstract:**

Model predictive control (MPC) is a widely used control design method
in the process industry. Its main advantage is that it allows the
inclusion of constraints on the inputs and outputs. Usually MPC uses
linear discrete-time models. We extend MPC to max-min-plus discrete
event systems. In general the resulting optimization problems are
nonlinear and nonconvex. However, if the state equations are decoupled
and if the control objective and the constraints depend monotonically
on the states and outputs of system, the max-min-plus-algebraic MPC
problem can be recast as problem with a convex feasible set. If in
addition the objective function is convex, this leads to a convex
optimization problem, which can be solved very efficiently.

Corresponding technical report: pdf file (134 KB)

@incollection{DeSvan:00-01,

author={B. {De Schutter} and T. {van den Boom}},

title={Model predictive control for max-min-plus systems},

booktitle={Discrete Event Systems: Analysis and Control \rm(Proceedings of the 5th International Workshop on Discrete Event Systems (WODES2000), Ghent, Belgium, Aug. 2000)},

series={The Kluwer International Series in Engineering and Computer Science},

volume={569},

editor={R. Boel and G. Stremersch},

publisher={Kluwer Academic Publishers},

address={Boston, Massachusetts},

pages={201--208},

year={2000}

}

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