Control of Manipulator Trajectory Tracking Based on Improved RBFNN

被引：1
|
作者：
Juan, Wei [2 ]
Yang, Huixian [1 ]
Xie, HaiXia [3 ]
机构：
[1] Xiangtan Univ, Fac Mat & Photoelect Phys, Xiangtan, Hunan, Peoples R China
[2] Xiangtan Univ, Coll Informat Engn, Xiangtan, Hunan, Peoples R China
[3] Qiongzhou Univ, Dept Phys, Wuzhishan, Hainan Province, Peoples R China
来源：
2009 INTERNATIONAL CONFERENCE ON INTELLIGENT HUMAN-MACHINE SYSTEMS AND CYBERNETICS, VOL 2, PROCEEDINGS | 2009年
基金：
海南省自然科学基金;
关键词：
trajectory tracking; radial basis function neural network (RBFNN); Fuzzy Genetic Algorithms (FGA); Nearest Neighbor Clustering Algorithms (NNCA);
D O I：
10.1109/IHMSC.2009.159
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In order to control the manipulator to track a given trajectory accurately and get good real-time performance put forward an improved RBF fuzzy neural network algorithm. In this algorithm, a novel Fuzzy Genetic Algorithm (FGA) was used to regulate the parameters of a neural fuzzy controller, make it optimized and a Nearest Neighbor Clustering Algorithms (NNCA) was adopted to refresh the fuzzy rules. In the simulation, compared with traditional fuzzy algorithms, this improved neural fuzzy algorithm gets better performance demonstrated, learning fast and tracking accurately.
引用
收藏
页码：142 / +
页数：2
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