Neural network saturation compensation for DC motor systems

被引：19
|
作者：
Jang, Jun Oh [1 ]
机构：
[1] Uiduk Univ, Dept Comp Control Engn, Kyongju 780713, South Korea
来源：
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS | 2007年 / 54卷 / 03期
关键词：
actuator nonlinearity; dc motor system; neural networks (NNs); saturation compensation; stability;
D O I：
10.1109/TIE.2007.894706
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
A neural network (NN) saturation compensation scheme for dc motor systems is presented. The scheme, which leads to stability, command following, and disturbance rejection, is rigorously proven. The online weight tuning law, overall closed-loop performance, and boundness of the NN weights are derived and guaranteed based on the Lyapunov approach. Simulation and experimental results show that the proposed scheme effectively compensates for saturation nonlinearity in the presence of system uncertainty.
引用
收藏
页码：1763 / 1767
页数：5
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