RBF Neural Network Control for Linear Motor-Direct Drive Actuator Based on an Extended State Observer

被引：4
|
作者：
Liu, Zhi [1 ]
Chen, Tefang [1 ]
机构：
[1] Cent S Univ, Sch Traff & Transportat Engn, Changsha, Hunan, Peoples R China
来源：
DISCRETE DYNAMICS IN NATURE AND SOCIETY | 2016年 / 2016卷
基金：
中国国家自然科学基金;
关键词：
DISTURBANCE REJECTION CONTROL; DESIGN;
D O I：
10.1155/2016/8390529
中图分类号：
O1 [数学];
学科分类号：
0701 ; 070101 ;
摘要：
Hydraulic power and other kinds of disturbance in a linear motor-direct drive actuator (LM-DDA) have a great impact on the performance of the system. A mathematical model of the LM-DDA system is established and a double-loop control system is presented. An extended state observer (ESO) with switched gain was utilized to estimate the influence of the hydraulic power and other load disturbances. Meanwhile, Radial Basis Function (RBF) neural network was utilized to optimize the parameters in this intelligent controller. The results of the dynamic tests demonstrate the performance with rapid response and improved accuracy could be attained by the proposed control scheme.
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收藏
页数：10
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