The trajectory planning and tracking of redundant manipulators by a hierarchical neurocontroller

Abstract

A hierarchical neurocontroller architecture, which comprises two artificial neural network (ANN) systems for inversion kinematics solution and motion control of robotic redundant manipulators is presented. The solution of inverse kinematics is realized by a Hopfield network, in which the global planning of a collision-free trajectory is based on the potential functions using the necessary conditions of minimum for an integral type criterion. A direct servo-level controller is utilized by a multilayer feedforward network based on backpropagation algorithm, in which the computed torque technique is employed to control manipulator's joints to track the trajectory. The stability of the both sub-controllers is analyzed. Another major contribution of this paper is to provide an approach for the most difficult problem in using neurocontroller-how to efficiently train the designed ANNs.