关键词: 逆运动学, 混沌粒子群, 差分算法, 多子群分层

Abstract: The mechanical arm control model established by using the D-H method of transformation matrix is seriously nonlinear, and it is difficult to solve through the traditional methods. The dynamic differential algorithm has the great global searching ability and the particle swarm optimization algorithm has the significant local searching accuracy. The paper mixes the improved dynamic differential algorithm and the particle swarm optimization algorithm, and adoptes the chaotic algorithm to initialize individuals’ values and the adaptive algorithm to modify the study facts and weight facts of particle swarm optimization. It presents multi-sub group hierarchical hybrid algorithm of differential algorithm and adaptive chaotic particle swarm optimization. The multi-subgroup hierarchical hybrid structure adopted by the paper can improves the ability of sharing the information among the individuals. The bottom structure uses the dynamic differential algorithm for the global searching, and the top elite population uses the improved adaptive chaotic particle swarm optimization for the local searching. The experimental simulation and practice result prove that the stability, the successful rate of searching and the convergence precision have been greatly improved. The discussed algorithm can solve the problem of the inverse kinematics model efficiently.

Key words: inverse kinematics, chaotic particle swarm optimization, differential algorithm, multi-subgroup hierarchical