A two-layer recurrent neural network for real-time control of redundant manipulators with torque minimization

A recurrent neural network for kinematic control of redundant robot manipulators with torque minimization is presented. The proposed recurrent neural network is composed of two bidirectionally connected layers of neuron arrays. While the command signals of desired acceleration of the end-effector are fed into the input layer, the output layer generates the joint acceleration vector of the manipulator with joint torques being minimized. The proposed recurrent neural network is shown to be capable of asymptotic tracking of trajectory for the redundant manipulators with minimized joint torques.