Title :
Optimal neuro control of robot manipulator
Author :
Nguyen Tran Hiep ; Pham Thuong Cat
Author_Institution :
Control Tech. Dept., Le Quy Don Univ., Hanoi, Vietnam
Abstract :
Recently, radial basis function network (RBFN) is used quite widely when using neural networks as controllers for subjects with multiple uncertain parameters such as the robot. The most important thing when using online learning neural network system is the choice of coefficient for networks with fast convergence speed. So far this coefficient has been chosen by experience and sometimes it takes quite a long time to find a coefficient that satisfies the requirement of the controlling task. Another problem is, when finding coefficients satisfying the required study of the problem and control, we can not conclude that the optimal coefficients. This article refers to the use of genetic algorithms (GA) to find optimal learning coefficient for RBF network is used as a controller for objects whose parameters are uncertain.
Keywords :
convergence; genetic algorithms; manipulators; neurocontrollers; optimal control; radial basis function networks; convergence speed; genetic algorithms; online learning neural network system; optimal learning coefficient; optimal neuro control; radial basis function network; robot manipulator; Genetic algorithms; Joints; Mathematical model; Radial basis function networks; Robot control; Genetic Algorithms; RBF networks;
Conference_Titel :
Control, Automation and Systems (ICCAS), 2011 11th International Conference on
Conference_Location :
Gyeonggi-do
Print_ISBN :
978-1-4577-0835-0