Remote network controller design based on fully tuned RBF neural network

被引：0
|
作者：
Hu, Yun-an [1 ]
Li, Jing [1 ]
Zuo, Bin [1 ]
机构：
[1] Naval Aeronaut Engn Inst, Dept Control Engn, Yantai 264001, Peoples R China
来源：
ICNC 2007: THIRD INTERNATIONAL CONFERENCE ON NATURAL COMPUTATION, VOL 2, PROCEEDINGS | 2007年
关键词：
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Considering a class of networked control systems (NCS) with generalized uncertainty and nonlinearities, a control strategy based on fully tuned RBF neural network(NN) feedback linearization and remote state feedback control is presented in the paper. Firstly, the weight W, center value phi and incidence a of the fully tuned RBF NN are designed to compensate the nonlinearities and generalized uncertainties. Then the state feedback control is utilized to control NCS with time-varying delay, and the stability of the closed-loop NCS is effectively guaranteed by Lyapunov stability theory. Finally, the simulation results show that this method is very effective.
引用
收藏
页码：445 / +
页数：2
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