A Hybrid Particle Swarm Optimization for Manipulator Inverse Kinematics Control

Authors:
Xiulan Wen;Danghong Sheng;Jiacai Huang
Affiliations:
Automation Department, Nanjing Institute of Technology, Nanjing, China 211167;Automation Department, Nanjing Institute of Technology, Nanjing, China 211167;Automation Department, Nanjing Institute of Technology, Nanjing, China 211167
Venue:
ICIC '08 Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Theoretical and Methodological Issues
Year:
2008

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Abstract

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.