Design of a neural network based self-tuning controller for an overhead crane

被引：0

作者：

Méndez, JA ^{[1
]}

Acosta, L ^{[1
]}

Moreno, L ^{[1
]}

Hamilton, A ^{[1
]}

Marichal, GN ^{[1
]}

机构：

[1] Univ La Laguna, Dept Appl Phys, La Laguna 38271, Tenerife, Spain

来源：

PROCEEDINGS OF THE 1998 IEEE INTERNATIONAL CONFERENCE ON CONTROL APPLICATIONS, VOLS 1 AND 2 | 1996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：

In the process industry, the use of overhead crane systems for the transportation of material is very common. These are non-linear systems that present undesirable oscillations during the motion, especially at arrival. This paper presents a self-tuning controller based on neural networks for the anti-swing control problem of the crane. The scheme of the controller is based on using neural networks as self-tuners for the parameters of a state feedback controller. The aim of this approach is to take advantage of the ability to learn of the neural networks and to use them in place of an identifier in the conventional self-tuner scheme. One of the main advantages of this method is that the training of the networks is done on-line using a backpropagation algorithm. The algorithm was implemented and tested by means of different simulations carried out with the crane.

引用

页码：168 / 171

页数：4

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