Min-max model predictive control for uncertain max-min-plus-scaling systems

被引：0

作者：

Necoara, Ion ^{[1
]}

De Schutter, Bart ^{[1
]}

van den Boom, Ton ^{[1
]}

Hellendoorn, Hans ^{[1
]}

机构：

[1] Delft Univ Technol, Delft Ctr Syst & Control, Delft, Netherlands

来源：

WODES 2006: EIGHTH INTERNATIONAL WORKSHOP ON DISCRETE EVENT SYSTEMS, PROCEEDINGS | 2006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：

We extend the model predictive control (MPC) framework that has been developed previously to a class of uncertain discrete event systems that can be modeled using the operations maximization, minimization, addition and scalar multiplication. This class encompasses max-plus-linear systems, min-max-plus systems, bilinear max-plus systems and polynomial max-plus systems. We first consider open-loop min-max MIPC and we show that the resulting optimization problem can be transformed into a set of linear programming problems. Then, min-max feedback model predictive control using disturbance feedback policies is presented, which leads to improved performance compared to the open-loop approach.

引用

页码：439 / +

页数：2

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