A hybrid particle swarm optimization for manipulator inverse kinematics control

A very important problem usually encountered in the study of robot manipulators is the inverse kinematics problem. The inverse kinematics control of a robotic manipulator requires solving non-linear equations having transcendental functions and involving time-consuming calculations. In this paper, a hybrid particle swarm optimization based on the behaviour of insect swarms and natural selection mechanism is firstly presented to optimize neural network (HPSONN) for manipulator inverse kinematics. Compared with the results of the fast back propagation learning algorithm (FBP), conventional genetic algorithm (GA) based elitist reservation (EGA), improved GA (IGA) and immune evolutionary computation (IEC), the simulation results verify the hybrid particle swarm optimization is more effective for manipulator inverse kinematics control than above most methods.