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

被引：59

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

页码：145 / 152

页数：8

共 13 条

[1]

Boyd S., 1994, STUDIES APPL NUMERIC

[2] Robust H∞ disturbance attenuation for a class of uncertain discrete-time fuzzy systems [J].