Globally Stable Adaptive Tracking Control Using RBF Neural Networks as Feedforward Compensator

被引：1
|
作者：
Chen, Weisheng [1 ]
Du, Zhenbin [2 ]
机构：
[1] Xidian Univ, Dept Appl Math, Xian 710071, Peoples R China
[2] Yantai Univ, Sch Comp Sci & Technol, Yantai 264005, Peoples R China
来源：
2010 8TH WORLD CONGRESS ON INTELLIGENT CONTROL AND AUTOMATION (WCICA) | 2010年
基金：
中国博士后科学基金; 中国国家自然科学基金;
关键词：
Adaptive tracking control; Backstepping; Feedforward compensators; Global stability; Neural networks; DISCRETE-TIME-SYSTEMS; UNCERTAIN NONLINEAR-SYSTEMS; OUTPUT-FEEDBACK CONTROL; DYNAMIC SURFACE CONTROL; NN CONTROL; BACKSTEPPING CONTROL; DELAY SYSTEMS; FORM; DISTURBANCES;
D O I：
10.1109/WCICA.2010.5554919
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
In this paper, it is showed that if neural networks are used as feedforward compensators instead of feedback ones, then we can ensure the global stability of closed-loop systems and determine the neural network approximation domain via the bound of known reference signals. It should be pointed out that this domain is very important for designing the neural network structure, for example, it directly determines the choice of the centers of radial basis function neural networks.
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页码：1067 / 1070
页数：4
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