Recurrent neural networks control of dynamic systems with unknown input hysteresis

被引：0
|
作者：
Wang, XS [1 ]
Li, L [1 ]
Su, CY [1 ]
Hong, H [1 ]
机构：
[1] Southeast Univ, Dept Mech Engn, Nanjing 210096, Jiangsu, Peoples R China
来源：
PROCEEDINGS OF 2003 INTERNATIONAL CONFERENCE ON NEURAL NETWORKS & SIGNAL PROCESSING, PROCEEDINGS, VOLS 1 AND 2 | 2003年
关键词：
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
This paper deals with the control of dynamic systems preceded by an unknown hysteresis, where the hysteresis is modeled by a differential equation. By exploiting proper-ties of the differential equation, a recurrent neural network is developed to construct a hysteresis inverse, which can compensate the affection of the input hysteresis. By using a traditional PD controller, the whole system will track a desired trajectory within a specified precision. Simulation results verified the proposed schemes.
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页码：297 / 300
页数：4
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