Neural-network-based payload determination of a moving loader

被引：8
|
作者：
Savia, M
Koivo, HN
机构：
[1] Tampere Univ Technol, Automat & Control Inst, FIN-33101 Tampere, Finland
[2] Helsinki Univ Technol, Control Engn Lab, Espoo 02015, Finland
来源：
CONTROL ENGINEERING PRACTICE | 2004年 / 12卷 / 05期
关键词：
Kalman filter; neural networks; payload estimation; intelligent mine;
D O I：
10.1016/S0967-0661(03)00136-9
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
This paper describes a method that combines a Kalman filter and neural network to form an efficient data fusion technique for estimating payload in the bucket of a moving loader. The Kalman filter is used to reduce the noise level in the measurement signals before the data are fed to the neural network. A neural network then represents the nonlinear connection between the indirect measurements describing the load and the actual load in the bucket. The results show that the combination of these different methods offers a viable solution for estimating the payload. (C) 2003 Elsevier Ltd. All rights reserved.
引用
收藏
页码：555 / 561
页数：7
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