A Lagrangian network for kinematic control of redundant robot manipulators

A recurrent neural network, called the Lagrangian network, is presented for the kinematic control of redundant robot manipulators, The optimal redunda ncy resolution is determined by the Lagrangian network through real-time so lution to the inverse kinematics problem formulated as a quadratic optimiza tion problem, While the signal for a desired velocity of the end-effector i s fed into the inputs of the Lagrangian network, it generates the joint vel ocity vector of the manipulator in its outputs along with the associated La grange multipliers. The proposed Lagrangian network is shown to be capable of asymptotic tracking for the motion control of kinematically redundant ma nipulators.