Discrete-Time Recurrent Neural DC Motor Control using Kalman Learning

被引：4
|
作者：
Castaneda, Carlos E. [1 ]
Sanchez, Edgar N. [1 ]
Loukianov, Alexander G. [1 ]
Castillo-Toledo, Bernardino [1 ]
机构：
[1] CINVESTAV, Unidad Guadalajara, Guadalajara 45090, Jalisco, Mexico
来源：
2008 IEEE INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, VOLS 1-8 | 2008年
关键词：
D O I：
10.1109/IJCNN.2008.4634062
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
An adaptive tracking controller for a discrete-time direct current (DC) motor model in presence of bounded disturbances is presented. A high order neural network is used to identify the plant model; this network is trained with an extended Kalman filter. Then, the discrete-time block control and sliding modes techniques are used to develop the reference tracking control. This paper includes also the respective stability analysis and a strategy to avoid specific adaptive weights zero-crossing. The scheme is illustrated via simulations for a discrete-time nonlinear model of an electric DC motor.
引用
收藏
页码：1930 / 1937
页数：8
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