Complexity reduction in MPC for stochastic max-plus-linear systems by variability expansion

被引：0
|
作者：
van den Boom, TJJ [1 ]
De Schutter, B [1 ]
Heidergott, B [1 ]
机构：
[1] Delft Univ Technol, Fac Informat Technol & Syst, NL-2600 GA Delft, Netherlands
来源：
PROCEEDINGS OF THE 41ST IEEE CONFERENCE ON DECISION AND CONTROL, VOLS 1-4 | 2002年
关键词：
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
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.
引用
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页码：3567 / 3572
页数：6
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