Neural network based min-max predictive control. Application to a heat exchanger

被引：0

作者：

Ramirez, DR ^{[1
]}

Arahal, MR ^{[1
]}

Camacho, EF ^{[1
]}

机构：

[1] Univ Sevilla, Escuela Super Ingn, Dept Ingn Sistemas & Automat, Seville 41092, Spain

来源：

ADAPTATION AND LEARNING IN CONTROL AND SIGNAL PROCESSING 2001 | 2002年

关键词：

predictive control; minimax techniques; neural networks; uncertain linear systems; process control;

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：

Min-max model predictive controllers (MMMPC) have been proposed for the control of linear plants subject to bounded uncertainties. The implementation of MMMPC suffers a large computational burden due to the numerical optimization problem that has to be solved at every sampling time. This fact severely limits the class of processes in which this control is suitable. In this paper the use of a Neural Network (NN) to approximate the solution of the min-max problem is proposed. The number of inputs of the NN is determined by the order and time delay of the model together with the control horizon. For large time delays the number of inputs can be prohibitive. A modification to the basic formulation is proposed in order to avoid this later problem. Simulation and experimental results are given using a heat exchanger. Copyright ((C)) 2001 IFAC.

引用

页码：115 / 120

页数：6

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