Inverse Kinematics Solution of Manipulator for the Steel Plate Forming by Line Cooling and Heating Based on Genetic Neural Network

Abstract

In this paper, we will analyze the forward and inverse kinematics of a manipulator for the Steel Plate Forming by Line Cooling and Heating (SPFCH). The forward kinematics of the manipulator is deduced firstly. Then the radial basis function (RBF) neural network algorithm is used to solve the inverse kinematics problems. The genetic algorithm is put forward for the optimization of RBF in view of the existing problems. The inverse kinematics difficulties can be overcome by the combination of RBF and genetic method (GA-RBF). At last, simulations obtained by using the GA-RBF method are presented. The results show that the method can achieve the tracking of the bending trajectory very well.