Infinity-norm torque minimization for redundant manipulators using a recurrent neural network

A recurrent neural network is applied for minimizing the infinity-norm of joint torques in redundant manipulators. The recurrent neural network explicitly minimizes the maximum component of joint torques in magnitude while keeping the relation between the joint torque and the end-effector acceleration satisfied. The end-effector accelerations are given to the recurrent neural network as its input, and the minimum infinity-norm joint torques is generated at the same time as its output. It is shown that the recurrent neural network is capable of effectively generating the minimum infinity-norm joint torque redundancy resolution of manipulators