Control of Manipulator Trajectory Tracking Based on Improved RBFNN

Author

Juan, Wei ; Yang, Huixian ; Xie, HaiXia

Author_Institution

Coll. of Inf. Eng., Xiangtan Univ., Xiangtan, China

Volume

2

fYear

2009

fDate

26-27 Aug. 2009

Firstpage

142

Lastpage

145

Abstract

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.

Keywords

fuzzy neural nets; genetic algorithms; manipulators; neurocontrollers; path planning; pattern clustering; radial basis function networks; RBF fuzzy neural network algorithm; RBFNN; fuzzy genetic algorithm; manipulator trajectory tracking; nearest neighbor clustering algorithms; neural fuzzy controller; Artificial neural networks; Clustering algorithms; Control systems; Fuzzy control; Fuzzy neural networks; Fuzzy systems; Input variables; Manipulator dynamics; Neural networks; Trajectory; Fuzzy Genetic Algorithms (FGA); Nearest Neighbor Clustering Algorithms (NNCA); radial basis function neural network (RBFNN); trajectory tracking;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Human-Machine Systems and Cybernetics, 2009. IHMSC '09. International Conference on

Conference_Location

Hangzhou, Zhejiang

Print_ISBN

978-0-7695-3752-8

Type

conf

DOI

10.1109/IHMSC.2009.159

Filename

5336026