Adaptive control design of neural fuzzy system for NARMA-L2 model

被引：0
|
作者：
Liu, Zhi [1 ]
Zhang, Yuri [1 ]
机构：
[1] Guangdong Univ Technol, Dept Automat, Guangzhou 510006, Peoples R China
来源：
WCICA 2006: SIXTH WORLD CONGRESS ON INTELLIGENT CONTROL AND AUTOMATION, VOLS 1-12, CONFERENCE PROCEEDINGS | 2006年
关键词：
nonlinear control; neural networks; adaptive control; Persistent Excitation;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
An adaptive neural control method is presented for the nonlinear discrete-time systems with the NARMA-L2 model. The neural fuzzy system is integrated with the approximate model-based control method to handle the nonlinear complexity, where the multiple fuzzy CMAC (MFCMAC) network is used to compensate the approximate NARMA model of the nonaffine nonlinear system. The weights of neural networks are modified by a novel adaptive algorithm, which guarantee the stability of the neural system without the persistent excitation requirement. The stability of the closed-loop system is proved with the Lyapunov method. Simulation results show that the method is effective.
引用
收藏
页码：2801 / +
页数：2
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