Model algorithm control using neural networks for input delayed nonlinear control system

被引:2
作者
Zhang, Yuanliang [1 ]
Chong, Kil To [2 ]
机构
[1] Huaihai Inst Technol, Sch Mech Engn, Lianyungang 222005, Peoples R China
[2] Chonbuk Natl Univ, Sch Elect & Informat, Jeonju 560756, South Korea
基金
新加坡国家研究基金会;
关键词
model algorithm control; neural network; nonlinear system; time delay; OUTPUT-FEEDBACK CONTROL; TIME-DELAY; ROBUST STABILIZATION; STABILITY ANALYSIS; LINEAR-SYSTEMS; DESIGN; OBSERVER;
D O I
10.1109/JSEE.2015.00019
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The performance of the model algorithm control method is partially based on the accuracy of the system's model. It is difficult to obtain a good model of a nonlinear system, especially when the nonlinearity is high. Neural networks have the ability to "learn" the characteristics of a system through nonlinear mapping to represent nonlinear functions as well as their inverse functions. This paper presents a model algorithm control method using neural networks for nonlinear time delay systems. Two neural networks are used in the control scheme. One neural network is trained as the model of the nonlinear time delay system, and the other one produces the control inputs. The neural networks are combined with the model algorithm control method to control the nonlinear time delay systems. Three examples are used to illustrate the proposed control method. The simulation results show that the proposed control method has a good control performance for nonlinear time delay systems.
引用
收藏
页码:142 / 150
页数:9
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