Title :
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
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;
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
DOI :
10.1109/IHMSC.2009.159
