A Nonlinear Optimal Iterative Learning Control Algorithm Based on RBF Neural Network and Clonal Selection Algorithm

被引：0
|
作者：
Li, Hengjie [1 ]
Hao, Xiaohong [1 ]
Pei, Xiping [1 ]
机构：
[1] Lanzhou Univ Technol, Coll Elect & Informat Engn, Lanzhou 730050, Gansu, Peoples R China
来源：
MATERIALS PROCESSING AND MANUFACTURING III, PTS 1-4 | 2013年 / 753-755卷
关键词：
Clonal Selection Algorithm; RBF neural networks; Iterative Learning Control;
D O I：
10.4028/www.scientific.net/AMR.753-755.1225
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
Improved clonal selection algorithms and RBF neural network are used for solving nonlinear optimization problems and modeling respectively in iterative learning control, and a nonlinear optimal iterative learning control algorithm (NOILCA) is proposed. In this method, an improved clonal selection algorithm is used for solving the optimum input for the next iteration; another one is used to update the RBF neural network model of real plant. Compared with GA-ILC, NOILCA has faster convergence speed, and is able to deal with the problem of inaccurate plant model, can obtain satisfactory tracking through the few several iterations.
引用
收藏
页码：1225 / 1229
页数：5
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