Model predictive control for max-plus-linear discrete event systems

被引：179
|
作者：
De Schutter, B [1 ]
van den Boom, T [1 ]
机构：
[1] Delft Univ Technol, Fac Informat Technol & Syst, Control Lab, NL-2600 GA Delft, Netherlands
来源：
AUTOMATICA | 2001年 / 37卷 / 07期
关键词：
discrete-event systems; predictive control; model-based control; generalized predictive control; max-plus-linear systems; max-plus algebra;
D O I：
10.1016/S0005-1098(01)00054-1
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 very popular controller design method in the process industry. A key advantage of MPC is that it can accommodate constraints on the inputs and outputs. Usually MPC uses linear discrete-rime models. In this paper we extend MPC to a class of discrete-event systems that can be described by models that are ''linear" in the max-plus algebra, which has maximization and addition as basic operations. In general. the resulting optimization problem are nonlinear and nonconvex. However, if the control objective and the constraints depend monotonically on the outputs of the system, the model predictive control 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. (C) 2001 Elsevier Science Ltd. All rights reserved.
引用
收藏
页码：1049 / 1056
页数：8
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