Bi-criteria torque optimization of redundant manipulators based on a simplified dual neural network

The bi-criteria joint torque optimization of kinematically redundant manipulators balances between the energy consumption and the torque distribution among the joints. In this paper, a simplified dual neural network is proposed to solve this problem. Joint torque limits are incorporated simultaneously into the proposed optimization scheme. The simplified dual network has less numbers of neurons compared with other recurrent neural networks and is proved to be globally convergent to optimal solutions. The control scheme based on the recurrent neural network is simulated with the PUMA 560 robot manipulator to demonstrate effectiveness.