Chaos control and synchronization, with input saturation, via recurrent neural networks

被引：31
|
作者：
Sanchez, EN [1 ]
Ricalde, LJ [1 ]
机构：
[1] Univ Guadalajara, CINVESTAV, Guadalajara 45091, Jalisco, Mexico
来源：
NEURAL NETWORKS | 2003年 / 16卷 / 5-6期
关键词：
recurrent neural networks; trajectory tracking; adaptive control; input saturation; Lyapunov function; stability;
D O I：
10.1016/S0893-6080(03)00122-9
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 adaptive tracking problem of non-linear systems in presence of unknown parameters, unmodelled dynamics and input saturation. A high order recurrent neural network is used in order to identify the unknown system and a learning law is obtained using the Lyapunov methodology. Then a stabilizing control law for the reference tracking error dynamics is developed using the Lyapunov methodology and the Sontag control law. Tracking error boundedness is established as a function of a design parameter. The new approach is illustrated by examples of complex dynamical systems: chaos control and synchronization. (C) 2003 Elsevier Science Ltd. All rights reserved.
引用
收藏
页码：711 / 717
页数：7
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