Reference:
T.J.J. van den Boom,
B. De Schutter, and
B. Heidergott,
"Complexity reduction in MPC for stochastic max-plus-linear systems by
variability expansion," Proceedings of the 41st IEEE Conference on
Decision and Control, Las Vegas, Nevada, pp. 3567-3572, Dec.
2002.
Abstract:
Model predictive control (MPC) is a popular controller design
technique in the process industry. Conventional MPC uses linear or
nonlinear discrete-time models. Recently, we have extended MPC to a
class of discrete event systems that can be described by a model that
is "linear" in the max-plus algebra. In our previous work we have
considered MPC for the perturbations-free case and for the case with
noise and/or modeling errors in a bounded or stochastic setting. In
this paper we consider a method to reduce the computational complexity
of the resulting optimization problem, based on variability expansion.
We show that the computational load is reduced if we decrease the
level of "randomness" in the system.
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