Fuzzy Control for Nonlinear Systems via Neural-Network-Based Approach

被引：52
|
作者：
Hsiao, Feng-Hsiag [1 ]
Chiang, Wei-Ling [2 ]
Chen, Cheng-Wu [2 ]
机构：
[1] Natl United Univ, Dept Elect Engn, Miaoli, Taiwan
[2] Natl Cent Univ, Dept Civil Engn, Chungli 320, Taiwan
来源：
INTERNATIONAL JOURNAL FOR COMPUTATIONAL METHODS IN ENGINEERING SCIENCE & MECHANICS | 2005年 / 6卷 / 03期
关键词：
Neural Network; Fuzzy Control;
D O I：
10.1080/15502280590923612
中图分类号：
O3 [力学];
学科分类号：
08 ; 0801 ;
摘要：
The stabilization problem is considered in this study for a nonlinear system. It is shown that the stability analysis of nonlinear systems can be reduced into linear matrix inequality (LMI) problems. First, the neural-network (NN) model is employed to approximate a nonlinear system via the backpropagation algorithm. Then, a linear differential inclusion (LDI) state-space representation is established for the dynamics of the NN model. In terms of Lyapunov's direct method, a sufficient condition is provided to guarantee the stability of nonlinear systems. Based on this criterion, a model-based fuzzy controller is then designed to stabilize the nonlinear system and the H-infinity control performance is achieved at the same time. Finally, two examples with numerical simulations are given to illustrate the control methodology.
引用
收藏
页码：145 / 152
页数：8
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